An R Markdown document converted from “./DT+Kmeans.ipynb”

Loading dataset

#loading the neceassary libraries 
suppressWarnings({
  library(dplyr)
library(readr)
library(caret)
library(tidyverse)
library(rpart)
    library(rattle)
library(rpart.plot)
library(RColorBrewer)
    library(FSelector)



})

## 
## Attaching package: 'dplyr'

## The following objects are masked from 'package:stats':
## 
##     filter, lag

## The following objects are masked from 'package:base':
## 
##     intersect, setdiff, setequal, union

## Loading required package: ggplot2

## Loading required package: lattice

## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## ✔ forcats   1.0.0     ✔ stringr   1.5.0
## ✔ lubridate 1.9.3     ✔ tibble    3.2.1
## ✔ purrr     1.0.2     ✔ tidyr     1.3.0
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag()    masks stats::lag()
## ✖ purrr::lift()   masks caret::lift()
## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors
## Loading required package: bitops
## 
## Rattle: A free graphical interface for data science with R.
## Version 5.5.1 Copyright (c) 2006-2021 Togaware Pty Ltd.
## Type 'rattle()' to shake, rattle, and roll your data.

dataset<-read_csv("depression_anxiety_data.csv")

## Rows: 783 Columns: 19
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (4): gender, who_bmi, depression_severity, anxiety_severity
## dbl (7): id, school_year, age, bmi, phq_score, gad_score, epworth_score
## lgl (8): depressiveness, suicidal, depression_diagnosis, depression_treatmen...
## 
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

################################### preproccing steps

##find missing values
is.na(dataset)

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## [783,] FALSE       FALSE FALSE  FALSE FALSE   FALSE     FALSE
##        depression_severity depressiveness suicidal depression_diagnosis
##   [1,]               FALSE          FALSE    FALSE                FALSE
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##        depression_treatment gad_score anxiety_severity anxiousness
##   [1,]                FALSE     FALSE            FALSE       FALSE
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## [377,]                FALSE     FALSE            FALSE       FALSE
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## [381,]                FALSE     FALSE            FALSE       FALSE
## [382,]                 TRUE     FALSE            FALSE        TRUE
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## [406,]                FALSE     FALSE            FALSE        TRUE
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## [720,]                FALSE     FALSE            FALSE       FALSE
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## [726,]                FALSE     FALSE            FALSE       FALSE
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## [728,]                FALSE     FALSE            FALSE       FALSE
## [729,]                FALSE     FALSE            FALSE       FALSE
## [730,]                FALSE     FALSE            FALSE       FALSE
## [731,]                FALSE     FALSE            FALSE       FALSE
## [732,]                FALSE     FALSE            FALSE       FALSE
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## [735,]                FALSE     FALSE            FALSE       FALSE
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## [753,]                FALSE     FALSE            FALSE       FALSE
## [754,]                FALSE     FALSE            FALSE       FALSE
## [755,]                FALSE     FALSE            FALSE       FALSE
## [756,]                FALSE     FALSE            FALSE       FALSE
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## [758,]                FALSE     FALSE            FALSE       FALSE
## [759,]                FALSE     FALSE            FALSE       FALSE
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## [762,]                FALSE     FALSE            FALSE       FALSE
## [763,]                FALSE     FALSE            FALSE       FALSE
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## [765,]                FALSE     FALSE            FALSE       FALSE
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## [768,]                FALSE     FALSE            FALSE       FALSE
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## [781,]                FALSE     FALSE            FALSE       FALSE
## [782,]                FALSE     FALSE            FALSE       FALSE
## [783,]                FALSE     FALSE            FALSE       FALSE
##        anxiety_diagnosis anxiety_treatment epworth_score sleepiness
##   [1,]             FALSE             FALSE         FALSE      FALSE
##   [2,]             FALSE             FALSE         FALSE      FALSE
##   [3,]             FALSE             FALSE         FALSE      FALSE
##   [4,]             FALSE             FALSE         FALSE      FALSE
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##  [30,]             FALSE             FALSE          TRUE       TRUE
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## [127,]             FALSE             FALSE         FALSE      FALSE
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## [669,]             FALSE             FALSE         FALSE      FALSE
## [670,]             FALSE             FALSE         FALSE      FALSE
## [671,]             FALSE             FALSE         FALSE      FALSE
## [672,]             FALSE             FALSE         FALSE      FALSE
## [673,]             FALSE             FALSE         FALSE      FALSE
## [674,]             FALSE             FALSE         FALSE      FALSE
## [675,]             FALSE             FALSE         FALSE      FALSE
## [676,]             FALSE             FALSE         FALSE      FALSE
## [677,]             FALSE             FALSE         FALSE      FALSE
## [678,]             FALSE             FALSE         FALSE      FALSE
## [679,]             FALSE             FALSE         FALSE      FALSE
## [680,]             FALSE             FALSE         FALSE      FALSE
## [681,]             FALSE             FALSE         FALSE      FALSE
## [682,]             FALSE             FALSE         FALSE      FALSE
## [683,]             FALSE             FALSE         FALSE      FALSE
## [684,]             FALSE             FALSE         FALSE      FALSE
## [685,]             FALSE             FALSE         FALSE      FALSE
## [686,]             FALSE             FALSE         FALSE      FALSE
## [687,]             FALSE             FALSE         FALSE      FALSE
## [688,]             FALSE             FALSE         FALSE      FALSE
## [689,]             FALSE             FALSE         FALSE      FALSE
## [690,]             FALSE             FALSE         FALSE      FALSE
## [691,]             FALSE             FALSE         FALSE      FALSE
## [692,]             FALSE             FALSE         FALSE      FALSE
## [693,]             FALSE             FALSE         FALSE      FALSE
## [694,]             FALSE             FALSE         FALSE      FALSE
## [695,]             FALSE             FALSE         FALSE      FALSE
## [696,]             FALSE             FALSE         FALSE      FALSE
## [697,]             FALSE             FALSE         FALSE      FALSE
## [698,]             FALSE             FALSE         FALSE      FALSE
## [699,]             FALSE             FALSE         FALSE      FALSE
## [700,]             FALSE             FALSE         FALSE      FALSE
## [701,]             FALSE             FALSE         FALSE      FALSE
## [702,]              TRUE             FALSE         FALSE      FALSE
## [703,]             FALSE             FALSE         FALSE      FALSE
## [704,]             FALSE             FALSE         FALSE      FALSE
## [705,]             FALSE             FALSE         FALSE      FALSE
## [706,]             FALSE             FALSE         FALSE      FALSE
## [707,]             FALSE             FALSE         FALSE      FALSE
## [708,]             FALSE             FALSE         FALSE      FALSE
## [709,]             FALSE             FALSE         FALSE      FALSE
## [710,]             FALSE             FALSE         FALSE      FALSE
## [711,]             FALSE             FALSE         FALSE      FALSE
## [712,]             FALSE             FALSE         FALSE      FALSE
## [713,]             FALSE             FALSE         FALSE      FALSE
## [714,]             FALSE             FALSE         FALSE      FALSE
## [715,]             FALSE             FALSE         FALSE      FALSE
## [716,]             FALSE             FALSE         FALSE      FALSE
## [717,]             FALSE             FALSE         FALSE      FALSE
## [718,]             FALSE             FALSE         FALSE      FALSE
## [719,]             FALSE             FALSE         FALSE      FALSE
## [720,]             FALSE             FALSE         FALSE      FALSE
## [721,]             FALSE             FALSE         FALSE      FALSE
## [722,]             FALSE             FALSE         FALSE      FALSE
## [723,]             FALSE             FALSE         FALSE      FALSE
## [724,]             FALSE             FALSE         FALSE      FALSE
## [725,]             FALSE             FALSE         FALSE      FALSE
## [726,]             FALSE             FALSE         FALSE      FALSE
## [727,]             FALSE             FALSE         FALSE      FALSE
## [728,]             FALSE             FALSE         FALSE      FALSE
## [729,]             FALSE             FALSE         FALSE      FALSE
## [730,]             FALSE             FALSE         FALSE      FALSE
## [731,]             FALSE             FALSE         FALSE      FALSE
## [732,]             FALSE             FALSE         FALSE      FALSE
## [733,]             FALSE             FALSE         FALSE      FALSE
## [734,]             FALSE             FALSE         FALSE      FALSE
## [735,]             FALSE             FALSE         FALSE      FALSE
## [736,]             FALSE             FALSE         FALSE      FALSE
## [737,]             FALSE             FALSE         FALSE      FALSE
## [738,]             FALSE             FALSE         FALSE      FALSE
## [739,]             FALSE             FALSE         FALSE      FALSE
## [740,]             FALSE             FALSE         FALSE      FALSE
## [741,]             FALSE             FALSE         FALSE      FALSE
## [742,]             FALSE             FALSE         FALSE      FALSE
## [743,]             FALSE             FALSE         FALSE      FALSE
## [744,]             FALSE             FALSE         FALSE      FALSE
## [745,]             FALSE             FALSE         FALSE      FALSE
## [746,]             FALSE             FALSE         FALSE      FALSE
## [747,]             FALSE             FALSE         FALSE      FALSE
## [748,]             FALSE             FALSE         FALSE      FALSE
## [749,]             FALSE             FALSE         FALSE      FALSE
## [750,]             FALSE             FALSE         FALSE      FALSE
## [751,]             FALSE             FALSE         FALSE      FALSE
## [752,]             FALSE             FALSE         FALSE      FALSE
## [753,]             FALSE             FALSE         FALSE      FALSE
## [754,]             FALSE             FALSE         FALSE      FALSE
## [755,]             FALSE             FALSE         FALSE      FALSE
## [756,]             FALSE             FALSE         FALSE      FALSE
## [757,]             FALSE             FALSE         FALSE      FALSE
## [758,]             FALSE             FALSE         FALSE      FALSE
## [759,]             FALSE             FALSE         FALSE      FALSE
## [760,]             FALSE             FALSE         FALSE      FALSE
## [761,]             FALSE             FALSE         FALSE      FALSE
## [762,]             FALSE             FALSE         FALSE      FALSE
## [763,]             FALSE             FALSE         FALSE      FALSE
## [764,]             FALSE             FALSE         FALSE      FALSE
## [765,]             FALSE             FALSE         FALSE      FALSE
## [766,]             FALSE             FALSE         FALSE      FALSE
## [767,]             FALSE             FALSE         FALSE      FALSE
## [768,]             FALSE             FALSE         FALSE      FALSE
## [769,]             FALSE             FALSE         FALSE      FALSE
## [770,]             FALSE             FALSE         FALSE      FALSE
## [771,]             FALSE             FALSE         FALSE      FALSE
## [772,]             FALSE             FALSE         FALSE      FALSE
## [773,]             FALSE             FALSE         FALSE      FALSE
## [774,]             FALSE             FALSE         FALSE      FALSE
## [775,]             FALSE             FALSE         FALSE      FALSE
## [776,]             FALSE             FALSE         FALSE      FALSE
## [777,]             FALSE             FALSE         FALSE      FALSE
## [778,]             FALSE             FALSE         FALSE      FALSE
## [779,]             FALSE             FALSE         FALSE      FALSE
## [780,]             FALSE             FALSE         FALSE      FALSE
## [781,]             FALSE             FALSE         FALSE      FALSE
## [782,]             FALSE             FALSE         FALSE      FALSE
## [783,]             FALSE             FALSE         FALSE      FALSE

##count missing values
sum(is.na(dataset))

## [1] 41

#delete the missing values
dataset[dataset=="Not Availble"]<- NA
dim(dataset)

## [1] 783  19

dataset = na.omit(dataset)
dim(dataset)

## [1] 757  19

sum(is.na(dataset))

## [1] 0

print(dataset)

## # A tibble: 757 × 19
##       id school_year   age gender   bmi who_bmi    phq_score depression_severity
##    <dbl>       <dbl> <dbl> <chr>  <dbl> <chr>          <dbl> <chr>              
##  1     1           1    19 male    33.3 Class I O…         9 Mild               
##  2     2           1    18 male    19.8 Normal             8 Mild               
##  3     3           1    19 male    25.1 Overweight         8 Mild               
##  4     4           1    18 female  23.7 Normal            19 Moderately severe  
##  5     5           1    18 male    25.6 Overweight         6 Mild               
##  6     6           1    18 male    22.1 Normal             3 None-minimal       
##  7     7           1    18 male    22.4 Normal             6 Mild               
##  8     8           1    19 male    20.5 Normal             4 None-minimal       
##  9     9           1    20 male    21.2 Normal            11 Moderate           
## 10    10           1    19 male    24.5 Normal             6 Mild               
## # ℹ 747 more rows
## # ℹ 11 more variables: depressiveness <lgl>, suicidal <lgl>,
## #   depression_diagnosis <lgl>, depression_treatment <lgl>, gad_score <dbl>,
## #   anxiety_severity <chr>, anxiousness <lgl>, anxiety_diagnosis <lgl>,
## #   anxiety_treatment <lgl>, epworth_score <dbl>, sleepiness <lgl>

i

#Detect Outliers 
#install.packages("outliers")
library(outliers)

Outage = outlier(dataset$age, logical =TRUE)
sum(Outage)

## [1] 2

Find_outlier = which(Outage ==TRUE, arr.ind = TRUE)
Outage

##   [1] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
##  [13] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
##  [25] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
##  [37] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
##  [49] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
##  [61] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
##  [73] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
##  [85] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
##  [97] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [109] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [121] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [133] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [145] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [157] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [169] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [181] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [193] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [205] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [217] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [229] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE
## [241] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [253] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [265] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [277] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [289] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [301] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [313] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [325] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [337] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [349] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [361] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [373] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [385] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [397] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [409] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [421] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [433] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [445] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [457] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [469] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [481] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [493] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [505] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [517] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [529] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [541] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [553] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [565] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [577] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [589] FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [601] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [613] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [625] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [637] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [649] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [661] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [673] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [685] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [697] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [709] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [721] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [733] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [745] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [757] FALSE

Find_outlier

## [1] 240 590

#
Outbmi= outlier(dataset$bmi, logical =TRUE)
sum(Outbmi)

## [1] 1

Find_outlier2 = which(Outbmi==TRUE, arr.ind = TRUE)
Outbmi

##   [1] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
##  [13] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
##  [25] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
##  [37] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
##  [49] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
##  [61] FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
##  [73] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
##  [85] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
##  [97] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [109] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [121] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [133] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [145] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [157] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [169] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [181] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [193] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [205] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [217] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [229] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [241] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [253] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [265] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [277] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [289] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [301] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [313] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [325] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [337] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [349] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [361] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [373] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [385] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [397] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [409] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [421] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [433] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [445] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [457] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [469] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [481] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [493] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [505] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [517] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [529] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [541] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [553] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [565] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [577] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [589] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [601] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [613] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [625] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [637] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [649] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [661] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [673] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [685] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [697] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [709] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [721] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [733] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [745] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [757] FALSE

Find_outlier2

## [1] 64

#delet outliers
dataset= dataset[-Find_outlier,]
dataset= dataset[-Find_outlier2,]

# data transformation (change the TRUR and False to  0 and 1 )


dataset$depressiveness = factor (dataset$depressiveness, levels = c(TRUE,FALSE), labels=c(1,0))
print(dataset)

## # A tibble: 754 × 19
##       id school_year   age gender   bmi who_bmi    phq_score depression_severity
##    <dbl>       <dbl> <dbl> <chr>  <dbl> <chr>          <dbl> <chr>              
##  1     1           1    19 male    33.3 Class I O…         9 Mild               
##  2     2           1    18 male    19.8 Normal             8 Mild               
##  3     3           1    19 male    25.1 Overweight         8 Mild               
##  4     4           1    18 female  23.7 Normal            19 Moderately severe  
##  5     5           1    18 male    25.6 Overweight         6 Mild               
##  6     6           1    18 male    22.1 Normal             3 None-minimal       
##  7     7           1    18 male    22.4 Normal             6 Mild               
##  8     8           1    19 male    20.5 Normal             4 None-minimal       
##  9     9           1    20 male    21.2 Normal            11 Moderate           
## 10    10           1    19 male    24.5 Normal             6 Mild               
## # ℹ 744 more rows
## # ℹ 11 more variables: depressiveness <fct>, suicidal <lgl>,
## #   depression_diagnosis <lgl>, depression_treatment <lgl>, gad_score <dbl>,
## #   anxiety_severity <chr>, anxiousness <lgl>, anxiety_diagnosis <lgl>,
## #   anxiety_treatment <lgl>, epworth_score <dbl>, sleepiness <lgl>

dataset$suicidal = factor (dataset$suicidal, levels = c(TRUE,FALSE), labels=c(1,0))
print(dataset)

## # A tibble: 754 × 19
##       id school_year   age gender   bmi who_bmi    phq_score depression_severity
##    <dbl>       <dbl> <dbl> <chr>  <dbl> <chr>          <dbl> <chr>              
##  1     1           1    19 male    33.3 Class I O…         9 Mild               
##  2     2           1    18 male    19.8 Normal             8 Mild               
##  3     3           1    19 male    25.1 Overweight         8 Mild               
##  4     4           1    18 female  23.7 Normal            19 Moderately severe  
##  5     5           1    18 male    25.6 Overweight         6 Mild               
##  6     6           1    18 male    22.1 Normal             3 None-minimal       
##  7     7           1    18 male    22.4 Normal             6 Mild               
##  8     8           1    19 male    20.5 Normal             4 None-minimal       
##  9     9           1    20 male    21.2 Normal            11 Moderate           
## 10    10           1    19 male    24.5 Normal             6 Mild               
## # ℹ 744 more rows
## # ℹ 11 more variables: depressiveness <fct>, suicidal <fct>,
## #   depression_diagnosis <lgl>, depression_treatment <lgl>, gad_score <dbl>,
## #   anxiety_severity <chr>, anxiousness <lgl>, anxiety_diagnosis <lgl>,
## #   anxiety_treatment <lgl>, epworth_score <dbl>, sleepiness <lgl>

dataset$depression_diagnosis = factor (dataset$depression_diagnosis, levels = c(TRUE,FALSE), labels=c(1,0))
print(dataset)

## # A tibble: 754 × 19
##       id school_year   age gender   bmi who_bmi    phq_score depression_severity
##    <dbl>       <dbl> <dbl> <chr>  <dbl> <chr>          <dbl> <chr>              
##  1     1           1    19 male    33.3 Class I O…         9 Mild               
##  2     2           1    18 male    19.8 Normal             8 Mild               
##  3     3           1    19 male    25.1 Overweight         8 Mild               
##  4     4           1    18 female  23.7 Normal            19 Moderately severe  
##  5     5           1    18 male    25.6 Overweight         6 Mild               
##  6     6           1    18 male    22.1 Normal             3 None-minimal       
##  7     7           1    18 male    22.4 Normal             6 Mild               
##  8     8           1    19 male    20.5 Normal             4 None-minimal       
##  9     9           1    20 male    21.2 Normal            11 Moderate           
## 10    10           1    19 male    24.5 Normal             6 Mild               
## # ℹ 744 more rows
## # ℹ 11 more variables: depressiveness <fct>, suicidal <fct>,
## #   depression_diagnosis <fct>, depression_treatment <lgl>, gad_score <dbl>,
## #   anxiety_severity <chr>, anxiousness <lgl>, anxiety_diagnosis <lgl>,
## #   anxiety_treatment <lgl>, epworth_score <dbl>, sleepiness <lgl>

dataset$depression_treatment = factor (dataset$depression_treatment, levels = c(TRUE,FALSE), labels=c(1,0))
print(dataset)

## # A tibble: 754 × 19
##       id school_year   age gender   bmi who_bmi    phq_score depression_severity
##    <dbl>       <dbl> <dbl> <chr>  <dbl> <chr>          <dbl> <chr>              
##  1     1           1    19 male    33.3 Class I O…         9 Mild               
##  2     2           1    18 male    19.8 Normal             8 Mild               
##  3     3           1    19 male    25.1 Overweight         8 Mild               
##  4     4           1    18 female  23.7 Normal            19 Moderately severe  
##  5     5           1    18 male    25.6 Overweight         6 Mild               
##  6     6           1    18 male    22.1 Normal             3 None-minimal       
##  7     7           1    18 male    22.4 Normal             6 Mild               
##  8     8           1    19 male    20.5 Normal             4 None-minimal       
##  9     9           1    20 male    21.2 Normal            11 Moderate           
## 10    10           1    19 male    24.5 Normal             6 Mild               
## # ℹ 744 more rows
## # ℹ 11 more variables: depressiveness <fct>, suicidal <fct>,
## #   depression_diagnosis <fct>, depression_treatment <fct>, gad_score <dbl>,
## #   anxiety_severity <chr>, anxiousness <lgl>, anxiety_diagnosis <lgl>,
## #   anxiety_treatment <lgl>, epworth_score <dbl>, sleepiness <lgl>

dataset$anxiousness = factor (dataset$anxiousness, levels = c(TRUE,FALSE), labels=c(1,0))
print(dataset)

## # A tibble: 754 × 19
##       id school_year   age gender   bmi who_bmi    phq_score depression_severity
##    <dbl>       <dbl> <dbl> <chr>  <dbl> <chr>          <dbl> <chr>              
##  1     1           1    19 male    33.3 Class I O…         9 Mild               
##  2     2           1    18 male    19.8 Normal             8 Mild               
##  3     3           1    19 male    25.1 Overweight         8 Mild               
##  4     4           1    18 female  23.7 Normal            19 Moderately severe  
##  5     5           1    18 male    25.6 Overweight         6 Mild               
##  6     6           1    18 male    22.1 Normal             3 None-minimal       
##  7     7           1    18 male    22.4 Normal             6 Mild               
##  8     8           1    19 male    20.5 Normal             4 None-minimal       
##  9     9           1    20 male    21.2 Normal            11 Moderate           
## 10    10           1    19 male    24.5 Normal             6 Mild               
## # ℹ 744 more rows
## # ℹ 11 more variables: depressiveness <fct>, suicidal <fct>,
## #   depression_diagnosis <fct>, depression_treatment <fct>, gad_score <dbl>,
## #   anxiety_severity <chr>, anxiousness <fct>, anxiety_diagnosis <lgl>,
## #   anxiety_treatment <lgl>, epworth_score <dbl>, sleepiness <lgl>

dataset$anxiety_diagnosis = factor (dataset$anxiety_diagnosis, levels = c(TRUE,FALSE), labels=c(1,0))
print(dataset)

## # A tibble: 754 × 19
##       id school_year   age gender   bmi who_bmi    phq_score depression_severity
##    <dbl>       <dbl> <dbl> <chr>  <dbl> <chr>          <dbl> <chr>              
##  1     1           1    19 male    33.3 Class I O…         9 Mild               
##  2     2           1    18 male    19.8 Normal             8 Mild               
##  3     3           1    19 male    25.1 Overweight         8 Mild               
##  4     4           1    18 female  23.7 Normal            19 Moderately severe  
##  5     5           1    18 male    25.6 Overweight         6 Mild               
##  6     6           1    18 male    22.1 Normal             3 None-minimal       
##  7     7           1    18 male    22.4 Normal             6 Mild               
##  8     8           1    19 male    20.5 Normal             4 None-minimal       
##  9     9           1    20 male    21.2 Normal            11 Moderate           
## 10    10           1    19 male    24.5 Normal             6 Mild               
## # ℹ 744 more rows
## # ℹ 11 more variables: depressiveness <fct>, suicidal <fct>,
## #   depression_diagnosis <fct>, depression_treatment <fct>, gad_score <dbl>,
## #   anxiety_severity <chr>, anxiousness <fct>, anxiety_diagnosis <fct>,
## #   anxiety_treatment <lgl>, epworth_score <dbl>, sleepiness <lgl>

dataset$anxiety_treatment = factor (dataset$anxiety_treatment, levels = c(TRUE,FALSE), labels=c(1,0))
print(dataset)

## # A tibble: 754 × 19
##       id school_year   age gender   bmi who_bmi    phq_score depression_severity
##    <dbl>       <dbl> <dbl> <chr>  <dbl> <chr>          <dbl> <chr>              
##  1     1           1    19 male    33.3 Class I O…         9 Mild               
##  2     2           1    18 male    19.8 Normal             8 Mild               
##  3     3           1    19 male    25.1 Overweight         8 Mild               
##  4     4           1    18 female  23.7 Normal            19 Moderately severe  
##  5     5           1    18 male    25.6 Overweight         6 Mild               
##  6     6           1    18 male    22.1 Normal             3 None-minimal       
##  7     7           1    18 male    22.4 Normal             6 Mild               
##  8     8           1    19 male    20.5 Normal             4 None-minimal       
##  9     9           1    20 male    21.2 Normal            11 Moderate           
## 10    10           1    19 male    24.5 Normal             6 Mild               
## # ℹ 744 more rows
## # ℹ 11 more variables: depressiveness <fct>, suicidal <fct>,
## #   depression_diagnosis <fct>, depression_treatment <fct>, gad_score <dbl>,
## #   anxiety_severity <chr>, anxiousness <fct>, anxiety_diagnosis <fct>,
## #   anxiety_treatment <fct>, epworth_score <dbl>, sleepiness <lgl>

dataset$sleepiness = factor (dataset$sleepiness, levels = c(TRUE,FALSE), labels=c(1,0))
print(dataset)

## # A tibble: 754 × 19
##       id school_year   age gender   bmi who_bmi    phq_score depression_severity
##    <dbl>       <dbl> <dbl> <chr>  <dbl> <chr>          <dbl> <chr>              
##  1     1           1    19 male    33.3 Class I O…         9 Mild               
##  2     2           1    18 male    19.8 Normal             8 Mild               
##  3     3           1    19 male    25.1 Overweight         8 Mild               
##  4     4           1    18 female  23.7 Normal            19 Moderately severe  
##  5     5           1    18 male    25.6 Overweight         6 Mild               
##  6     6           1    18 male    22.1 Normal             3 None-minimal       
##  7     7           1    18 male    22.4 Normal             6 Mild               
##  8     8           1    19 male    20.5 Normal             4 None-minimal       
##  9     9           1    20 male    21.2 Normal            11 Moderate           
## 10    10           1    19 male    24.5 Normal             6 Mild               
## # ℹ 744 more rows
## # ℹ 11 more variables: depressiveness <fct>, suicidal <fct>,
## #   depression_diagnosis <fct>, depression_treatment <fct>, gad_score <dbl>,
## #   anxiety_severity <chr>, anxiousness <fct>, anxiety_diagnosis <fct>,
## #   anxiety_treatment <fct>, epworth_score <dbl>, sleepiness <fct>

#Discretization the values based on fixed set of predetermined criteria

#1- bmi discretizatio (indicate high body fatness)

breaks <- c(18,18.4,24.9,29.9,34.9,39.9,100)
dataset$bmi= cut(dataset$bmi, breaks = breaks)
print(dataset)

## # A tibble: 754 × 19
##       id school_year   age gender bmi      who_bmi phq_score depression_severity
##    <dbl>       <dbl> <dbl> <chr>  <fct>    <chr>       <dbl> <chr>              
##  1     1           1    19 male   (29.9,3… Class …         9 Mild               
##  2     2           1    18 male   (18.4,2… Normal          8 Mild               
##  3     3           1    19 male   (24.9,2… Overwe…         8 Mild               
##  4     4           1    18 female (18.4,2… Normal         19 Moderately severe  
##  5     5           1    18 male   (24.9,2… Overwe…         6 Mild               
##  6     6           1    18 male   (18.4,2… Normal          3 None-minimal       
##  7     7           1    18 male   (18.4,2… Normal          6 Mild               
##  8     8           1    19 male   (18.4,2… Normal          4 None-minimal       
##  9     9           1    20 male   (18.4,2… Normal         11 Moderate           
## 10    10           1    19 male   (18.4,2… Normal          6 Mild               
## # ℹ 744 more rows
## # ℹ 11 more variables: depressiveness <fct>, suicidal <fct>,
## #   depression_diagnosis <fct>, depression_treatment <fct>, gad_score <dbl>,
## #   anxiety_severity <chr>, anxiousness <fct>, anxiety_diagnosis <fct>,
## #   anxiety_treatment <fct>, epworth_score <dbl>, sleepiness <fct>

#2- phq test score discretizatio(Depression Test Questionnaire)

breaks <- c(0,4,9,14,19,27)
dataset$phq_score= cut(dataset$phq_score, breaks = breaks)
print(dataset)

## # A tibble: 754 × 19
##       id school_year   age gender bmi      who_bmi phq_score depression_severity
##    <dbl>       <dbl> <dbl> <chr>  <fct>    <chr>   <fct>     <chr>              
##  1     1           1    19 male   (29.9,3… Class … (4,9]     Mild               
##  2     2           1    18 male   (18.4,2… Normal  (4,9]     Mild               
##  3     3           1    19 male   (24.9,2… Overwe… (4,9]     Mild               
##  4     4           1    18 female (18.4,2… Normal  (14,19]   Moderately severe  
##  5     5           1    18 male   (24.9,2… Overwe… (4,9]     Mild               
##  6     6           1    18 male   (18.4,2… Normal  (0,4]     None-minimal       
##  7     7           1    18 male   (18.4,2… Normal  (4,9]     Mild               
##  8     8           1    19 male   (18.4,2… Normal  (0,4]     None-minimal       
##  9     9           1    20 male   (18.4,2… Normal  (9,14]    Moderate           
## 10    10           1    19 male   (18.4,2… Normal  (4,9]     Mild               
## # ℹ 744 more rows
## # ℹ 11 more variables: depressiveness <fct>, suicidal <fct>,
## #   depression_diagnosis <fct>, depression_treatment <fct>, gad_score <dbl>,
## #   anxiety_severity <chr>, anxiousness <fct>, anxiety_diagnosis <fct>,
## #   anxiety_treatment <fct>, epworth_score <dbl>, sleepiness <fct>

#3- gad test score discretization(Generalised Anxiety Disorder Assessment)


breaks <- c(0,4,9,14,21)
dataset$gad_score= cut(dataset$gad_score, breaks = breaks)
print(dataset)

## # A tibble: 754 × 19
##       id school_year   age gender bmi      who_bmi phq_score depression_severity
##    <dbl>       <dbl> <dbl> <chr>  <fct>    <chr>   <fct>     <chr>              
##  1     1           1    19 male   (29.9,3… Class … (4,9]     Mild               
##  2     2           1    18 male   (18.4,2… Normal  (4,9]     Mild               
##  3     3           1    19 male   (24.9,2… Overwe… (4,9]     Mild               
##  4     4           1    18 female (18.4,2… Normal  (14,19]   Moderately severe  
##  5     5           1    18 male   (24.9,2… Overwe… (4,9]     Mild               
##  6     6           1    18 male   (18.4,2… Normal  (0,4]     None-minimal       
##  7     7           1    18 male   (18.4,2… Normal  (4,9]     Mild               
##  8     8           1    19 male   (18.4,2… Normal  (0,4]     None-minimal       
##  9     9           1    20 male   (18.4,2… Normal  (9,14]    Moderate           
## 10    10           1    19 male   (18.4,2… Normal  (4,9]     Mild               
## # ℹ 744 more rows
## # ℹ 11 more variables: depressiveness <fct>, suicidal <fct>,
## #   depression_diagnosis <fct>, depression_treatment <fct>, gad_score <fct>,
## #   anxiety_severity <chr>, anxiousness <fct>, anxiety_diagnosis <fct>,
## #   anxiety_treatment <fct>, epworth_score <dbl>, sleepiness <fct>

#4- Epworth test score discretizatio (Sleepiness Scale)

breaks <- c(0,10,14,17,24)
dataset$epworth_score= cut(dataset$epworth_score, breaks = breaks)
print(dataset)

## # A tibble: 754 × 19
##       id school_year   age gender bmi      who_bmi phq_score depression_severity
##    <dbl>       <dbl> <dbl> <chr>  <fct>    <chr>   <fct>     <chr>              
##  1     1           1    19 male   (29.9,3… Class … (4,9]     Mild               
##  2     2           1    18 male   (18.4,2… Normal  (4,9]     Mild               
##  3     3           1    19 male   (24.9,2… Overwe… (4,9]     Mild               
##  4     4           1    18 female (18.4,2… Normal  (14,19]   Moderately severe  
##  5     5           1    18 male   (24.9,2… Overwe… (4,9]     Mild               
##  6     6           1    18 male   (18.4,2… Normal  (0,4]     None-minimal       
##  7     7           1    18 male   (18.4,2… Normal  (4,9]     Mild               
##  8     8           1    19 male   (18.4,2… Normal  (0,4]     None-minimal       
##  9     9           1    20 male   (18.4,2… Normal  (9,14]    Moderate           
## 10    10           1    19 male   (18.4,2… Normal  (4,9]     Mild               
## # ℹ 744 more rows
## # ℹ 11 more variables: depressiveness <fct>, suicidal <fct>,
## #   depression_diagnosis <fct>, depression_treatment <fct>, gad_score <fct>,
## #   anxiety_severity <chr>, anxiousness <fct>, anxiety_diagnosis <fct>,
## #   anxiety_treatment <fct>, epworth_score <fct>, sleepiness <fct>

######### end of preproccing 

########### diagram for the class label (depressivness)
boolean_data <- dataset$depressiveness
print(boolean_data)

##   [1] 0 0 0 1 0 0 0 0 1 0 0 0 0 1 0 0 0 0 0 0 0 0 0 1 0 1 0 0 0 0 0 0 0 0 0 0 0
##  [38] 0 1 0 1 0 0 0 0 0 1 0 1 0 1 0 0 0 0 0 0 0 1 0 1 0 1 0 1 0 0 1 1 0 0 0 0 0
##  [75] 0 0 0 0 1 0 0 1 1 1 0 0 0 0 0 0 1 0 1 0 0 0 0 0 0 0 0 1 0 0 0 1 0 1 1 0 0
## [112] 0 0 0 0 0 0 1 0 1 1 1 1 0 0 1 1 0 1 0 1 0 1 1 0 0 1 1 1 0 0 0 1 0 1 1 0 0
## [149] 1 0 0 0 0 0 1 0 1 0 0 0 0 0 1 0 1 1 0 0 0 0 0 0 0 0 0 1 1 0 1 0 0 1 0 1 1
## [186] 0 0 0 0 1 1 0 1 1 1 0 1 0 0 0 0 0 0 0 1 0 0 0 1 0 0 0 0 1 0 0 1 0 0 1 0 1
## [223] 1 1 1 1 0 1 1 0 0 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 1 1 0 1 0 1 0 0 0 0 0 0 0
## [260] 0 0 1 0 0 0 0 0 0 0 0 1 0 0 1 1 1 1 0 1 0 1 0 1 0 0 1 0 1 1 0 1 1 1 0 0 1
## [297] 1 0 0 1 1 1 0 1 0 0 1 0 0 1 1 0 1 0 1 0 0 0 0 0 0 0 0 0 1 0 0 1 1 1 0 1 0
## [334] 0 0 0 1 0 0 0 0 0 0 0 0 1 1 1 0 1 0 0 0 0 0 0 0 0 1 0 1 0 0 0 1 0 0 0 0 1
## [371] 0 1 0 0 0 1 0 0 0 0 0 0 1 0 0 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 0 0 0 1
## [408] 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 1 0 0 1 1 1 0 0 1 0 0 0 0 0 0 0 0 1 0 0
## [445] 0 1 0 0 0 0 0 1 1 1 0 0 0 0 1 0 1 0 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [482] 0 0 1 0 1 0 1 0 0 1 0 0 0 0 0 1 0 1 0 0 1 0 0 1 0 0 0 0 0 1 1 0 0 0 0 0 0
## [519] 0 0 1 0 0 0 1 0 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 1 0 0 0 0 0 0
## [556] 1 1 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0
## [593] 0 0 0 0 1 1 0 0 0 0 1 1 0 1 0 0 0 0 0 0 0 1 0 1 0 1 1 0 0 0 0 0 0 0 1 0 0
## [630] 0 0 0 0 1 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 1 1 0 0 0 1 0 0 0 1 1 0 0 0
## [667] 1 0 0 0 1 1 0 0 0 0 0 0 0 1 0 1 0 0 0 1 0 1 1 0 0 1 0 1 0 1 1 0 0 1 1 0 0
## [704] 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 1
## [741] 0 0 0 0 0 0 0 0 0 0 0 0 1 0
## Levels: 1 0

# Count the occurrences of TRUE and FALSE values
true_count <- sum(boolean_data==1)
print(true_count)

## [1] 203

false_count <- sum(boolean_data==0) 
print(false_count)

## [1] 551

# Values for the bar chart
bar_heights <- c(true_count, false_count)

# Names for the bars
bar_names <- c("TRUE", "FALSE")

# Create a bar chart
barplot(bar_heights, names.arg = bar_names, col = c("blue", "red"),
        xlab = "Boolean Values", ylab = "Count",
        main = "class label (depressiveness)")

Building model

# explore the dataset 
str(dataset)

## tibble [754 × 19] (S3: tbl_df/tbl/data.frame)
##  $ id                  : num [1:754] 1 2 3 4 5 6 7 8 9 10 ...
##  $ school_year         : num [1:754] 1 1 1 1 1 1 1 1 1 1 ...
##  $ age                 : num [1:754] 19 18 19 18 18 18 18 19 20 19 ...
##  $ gender              : chr [1:754] "male" "male" "male" "female" ...
##  $ bmi                 : Factor w/ 6 levels "(18,18.4]","(18.4,24.9]",..: 4 2 3 2 3 2 2 2 2 2 ...
##  $ who_bmi             : chr [1:754] "Class I Obesity" "Normal" "Overweight" "Normal" ...
##  $ phq_score           : Factor w/ 5 levels "(0,4]","(4,9]",..: 2 2 2 4 2 1 2 1 3 2 ...
##  $ depression_severity : chr [1:754] "Mild" "Mild" "Mild" "Moderately severe" ...
##  $ depressiveness      : Factor w/ 2 levels "1","0": 2 2 2 1 2 2 2 2 1 2 ...
##  $ suicidal            : Factor w/ 2 levels "1","0": 2 2 2 1 2 2 2 2 2 2 ...
##  $ depression_diagnosis: Factor w/ 2 levels "1","0": 2 2 2 2 2 2 2 2 2 2 ...
##  $ depression_treatment: Factor w/ 2 levels "1","0": 2 2 2 2 2 2 2 2 2 2 ...
##  $ gad_score           : Factor w/ 4 levels "(0,4]","(4,9]",..: 3 2 2 4 3 1 1 2 2 1 ...
##  $ anxiety_severity    : chr [1:754] "Moderate" "Mild" "Mild" "Severe" ...
##  $ anxiousness         : Factor w/ 2 levels "1","0": 1 2 2 1 1 2 2 2 2 2 ...
##  $ anxiety_diagnosis   : Factor w/ 2 levels "1","0": 2 2 2 2 2 2 2 2 2 2 ...
##  $ anxiety_treatment   : Factor w/ 2 levels "1","0": 2 2 2 2 2 2 2 2 2 2 ...
##  $ epworth_score       : Factor w/ 4 levels "(0,10]","(10,14]",..: 1 2 1 2 1 1 1 1 1 1 ...
##  $ sleepiness          : Factor w/ 2 levels "1","0": 2 1 2 1 2 2 2 2 2 2 ...
##  - attr(*, "na.action")= 'omit' Named int [1:26] 12 23 24 25 30 40 51 128 158 167 ...
##   ..- attr(*, "names")= chr [1:26] "12" "23" "24" "25" ...

glimpse(dataset)

## Rows: 754
## Columns: 19
## $ id                   <dbl> 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 13, 14, 15, 16…
## $ school_year          <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1…
## $ age                  <dbl> 19, 18, 19, 18, 18, 18, 18, 19, 20, 19, 18, 19, 1…
## $ gender               <chr> "male", "male", "male", "female", "male", "male",…
## $ bmi                  <fct> "(29.9,34.9]", "(18.4,24.9]", "(24.9,29.9]", "(18…
## $ who_bmi              <chr> "Class I Obesity", "Normal", "Overweight", "Norma…
## $ phq_score            <fct> "(4,9]", "(4,9]", "(4,9]", "(14,19]", "(4,9]", "(…
## $ depression_severity  <chr> "Mild", "Mild", "Mild", "Moderately severe", "Mil…
## $ depressiveness       <fct> 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0…
## $ suicidal             <fct> 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
## $ depression_diagnosis <fct> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
## $ depression_treatment <fct> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
## $ gad_score            <fct> "(9,14]", "(4,9]", "(4,9]", "(14,21]", "(9,14]", …
## $ anxiety_severity     <chr> "Moderate", "Mild", "Mild", "Severe", "Moderate",…
## $ anxiousness          <fct> 1, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0…
## $ anxiety_diagnosis    <fct> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
## $ anxiety_treatment    <fct> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
## $ epworth_score        <fct> "(0,10]", "(10,14]", "(0,10]", "(10,14]", "(0,10]…
## $ sleepiness           <fct> 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0…

Steps involved in the K-fold Cross Validation in R:

• Split the data set into K subsets randomly
• For each one of the developed subsets of data points
• treat that subset as the validation (test) set
• Use all the rest subsets for training purpose
• Training of the model and evaluate it on the validation set or test set
• Calculate prediction error
• Repeat the above step K times i.e., until the model is not trained and tested on all subsets
• Generate overall prediction error by taking the average of prediction errors in every case

# setting seed to generate a  
# reproducible random sampling
set.seed(123)

# there is still missing value 
dataset <- na.omit(dataset)

#  make sure that the outcome column is a factor or numeric.
# we need to make sure the class lable is factor 
str(dataset$depressiveness)

##  Factor w/ 2 levels "1","0": 2 2 2 1 2 2 2 2 1 2 ...

# we can see the datatype is binary as true and false 
#convert it to factor
dataset$depressiveness <- as.factor(dataset$depressiveness)

DT with k-validation 5 and attribute selection information gain, gain ratio , gini index

#make data frame to track the process of the different tree 
# Create an empty dataframe
evaluation_df <- data.frame(tree = character(), accuracy = numeric(), Atrribute=character(),cv_K=character(),stringsAsFactors = FALSE)

Spilt the data into k volds

folds <- createFolds(dataset$depressiveness, k = 5)  # create the folds
# control parameters 
train_control <- trainControl(method = "cv", index = folds)

information gain

#build the model
tree1 <- train(depressiveness ~ ., data = dataset, trControl = train_control, 
               method = "rpart", parms = list(split = "information"))

# train result
trained_model <- tree1$finalModel
trained_model

## n= 677 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 677 194 0 (0.28655835 0.71344165)  
##   2) phq_score(9,14]>=0.5 130   0 1 (1.00000000 0.00000000) *
##   3) phq_score(9,14]< 0.5 547  64 0 (0.11700183 0.88299817)  
##     6) suicidal0< 0.5 42   0 1 (1.00000000 0.00000000) *
##     7) suicidal0>=0.5 505  22 0 (0.04356436 0.95643564) *

#features importance
trained_model$variable.importance

## depression_severityModerate             phq_score(9,14] 
##                  208.133991                  208.133991 
##                   suicidal0   depression_severitySevere 
##                  106.965905                   15.280844 
##            phq_score(19,27]        epworth_score(17,24] 
##                   15.280844                   10.187229 
##               bmi(39.9,100]    who_bmiClass III Obesity 
##                    3.202061                    3.202061 
##              bmi(34.9,39.9]     who_bmiClass II Obesity 
##                    2.546807                    2.546807

#summary of the trainig 
summary(trained_model)

## Call:
## (function (formula, data, weights, subset, na.action = na.rpart, 
##     method, model = FALSE, x = FALSE, y = TRUE, parms, control, 
##     cost, ...) 
## {
##     Call <- match.call()
##     if (is.data.frame(model)) {
##         m <- model
##         model <- FALSE
##     }
##     else {
##         indx <- match(c("formula", "data", "weights", "subset"), 
##             names(Call), nomatch = 0)
##         if (indx[1] == 0) 
##             stop("a 'formula' argument is required")
##         temp <- Call[c(1, indx)]
##         temp$na.action <- na.action
##         temp[[1]] <- quote(stats::model.frame)
##         m <- eval.parent(temp)
##     }
##     Terms <- attr(m, "terms")
##     if (any(attr(Terms, "order") > 1)) 
##         stop("Trees cannot handle interaction terms")
##     Y <- model.response(m)
##     wt <- model.weights(m)
##     if (any(wt < 0)) 
##         stop("negative weights not allowed")
##     if (!length(wt)) 
##         wt <- rep(1, nrow(m))
##     offset <- model.offset(m)
##     X <- rpart.matrix(m)
##     nobs <- nrow(X)
##     nvar <- ncol(X)
##     if (missing(method)) {
##         method <- if (is.factor(Y) || is.character(Y)) 
##             "class"
##         else if (inherits(Y, "Surv")) 
##             "exp"
##         else if (is.matrix(Y)) 
##             "poisson"
##         else "anova"
##     }
##     if (is.list(method)) {
##         mlist <- method
##         method <- "user"
##         init <- if (missing(parms)) 
##             mlist$init(Y, offset, wt = wt)
##         else mlist$init(Y, offset, parms, wt)
##         keep <- rpartcallback(mlist, nobs, init)
##         method.int <- 4
##         parms <- init$parms
##     }
##     else {
##         method.int <- pmatch(method, c("anova", "poisson", "class", 
##             "exp"))
##         if (is.na(method.int)) 
##             stop("Invalid method")
##         method <- c("anova", "poisson", "class", "exp")[method.int]
##         if (method.int == 4) 
##             method.int <- 2
##         init <- if (missing(parms)) 
##             get(paste("rpart", method, sep = "."), envir = environment())(Y, 
##                 offset, , wt)
##         else get(paste("rpart", method, sep = "."), envir = environment())(Y, 
##             offset, parms, wt)
##         ns <- asNamespace("rpart")
##         if (!is.null(init$print)) 
##             environment(init$print) <- ns
##         if (!is.null(init$summary)) 
##             environment(init$summary) <- ns
##         if (!is.null(init$text)) 
##             environment(init$text) <- ns
##     }
##     Y <- init$y
##     xlevels <- .getXlevels(Terms, m)
##     cats <- rep(0, ncol(X))
##     if (!is.null(xlevels)) {
##         indx <- match(names(xlevels), colnames(X), nomatch = 0)
##         cats[indx] <- (unlist(lapply(xlevels, length)))[indx > 
##             0]
##     }
##     extraArgs <- list(...)
##     if (length(extraArgs)) {
##         controlargs <- names(formals(rpart.control))
##         indx <- match(names(extraArgs), controlargs, nomatch = 0)
##         if (any(indx == 0)) 
##             stop(gettextf("Argument %s not matched", names(extraArgs)[indx == 
##                 0]), domain = NA)
##     }
##     controls <- rpart.control(...)
##     if (!missing(control)) 
##         controls[names(control)] <- control
##     xval <- controls$xval
##     if (is.null(xval) || (length(xval) == 1 && xval == 0) || 
##         method == "user") {
##         xgroups <- 0
##         xval <- 0
##     }
##     else if (length(xval) == 1) {
##         xgroups <- sample(rep(1:xval, length.out = nobs), nobs, 
##             replace = FALSE)
##     }
##     else if (length(xval) == nobs) {
##         xgroups <- xval
##         xval <- length(unique(xgroups))
##     }
##     else {
##         if (!is.null(attr(m, "na.action"))) {
##             temp <- as.integer(attr(m, "na.action"))
##             xval <- xval[-temp]
##             if (length(xval) == nobs) {
##                 xgroups <- xval
##                 xval <- length(unique(xgroups))
##             }
##             else stop("Wrong length for 'xval'")
##         }
##         else stop("Wrong length for 'xval'")
##     }
##     if (missing(cost)) 
##         cost <- rep(1, nvar)
##     else {
##         if (length(cost) != nvar) 
##             stop("Cost vector is the wrong length")
##         if (any(cost <= 0)) 
##             stop("Cost vector must be positive")
##     }
##     tfun <- function(x) if (is.matrix(x)) 
##         rep(is.ordered(x), ncol(x))
##     else is.ordered(x)
##     labs <- sub("^`(.*)`$", "\\1", attr(Terms, "term.labels"))
##     isord <- unlist(lapply(m[labs], tfun))
##     storage.mode(X) <- "double"
##     storage.mode(wt) <- "double"
##     temp <- as.double(unlist(init$parms))
##     if (!length(temp)) 
##         temp <- 0
##     rpfit <- .Call(C_rpart, ncat = as.integer(cats * !isord), 
##         method = as.integer(method.int), as.double(unlist(controls)), 
##         temp, as.integer(xval), as.integer(xgroups), as.double(t(init$y)), 
##         X, wt, as.integer(init$numy), as.double(cost))
##     nsplit <- nrow(rpfit$isplit)
##     ncat <- if (!is.null(rpfit$csplit)) 
##         nrow(rpfit$csplit)
##     else 0
##     if (nsplit == 0) 
##         xval <- 0
##     numcp <- ncol(rpfit$cptable)
##     temp <- if (nrow(rpfit$cptable) == 3) 
##         c("CP", "nsplit", "rel error")
##     else c("CP", "nsplit", "rel error", "xerror", "xstd")
##     dimnames(rpfit$cptable) <- list(temp, 1:numcp)
##     tname <- c("<leaf>", colnames(X))
##     splits <- matrix(c(rpfit$isplit[, 2:3], rpfit$dsplit), ncol = 5, 
##         dimnames = list(tname[rpfit$isplit[, 1] + 1], c("count", 
##             "ncat", "improve", "index", "adj")))
##     index <- rpfit$inode[, 2]
##     nadd <- sum(isord[rpfit$isplit[, 1]])
##     if (nadd > 0) {
##         newc <- matrix(0, nadd, max(cats))
##         cvar <- rpfit$isplit[, 1]
##         indx <- isord[cvar]
##         cdir <- splits[indx, 2]
##         ccut <- floor(splits[indx, 4])
##         splits[indx, 2] <- cats[cvar[indx]]
##         splits[indx, 4] <- ncat + 1:nadd
##         for (i in 1:nadd) {
##             newc[i, 1:(cats[(cvar[indx])[i]])] <- -as.integer(cdir[i])
##             newc[i, 1:ccut[i]] <- as.integer(cdir[i])
##         }
##         catmat <- if (ncat == 0) 
##             newc
##         else {
##             cs <- rpfit$csplit
##             ncs <- ncol(cs)
##             ncc <- ncol(newc)
##             if (ncs < ncc) 
##                 cs <- cbind(cs, matrix(0, nrow(cs), ncc - ncs))
##             rbind(cs, newc)
##         }
##         ncat <- ncat + nadd
##     }
##     else catmat <- rpfit$csplit
##     if (nsplit == 0) {
##         frame <- data.frame(row.names = 1, var = "<leaf>", n = rpfit$inode[, 
##             5], wt = rpfit$dnode[, 3], dev = rpfit$dnode[, 1], 
##             yval = rpfit$dnode[, 4], complexity = rpfit$dnode[, 
##                 2], ncompete = 0, nsurrogate = 0)
##     }
##     else {
##         temp <- ifelse(index == 0, 1, index)
##         svar <- ifelse(index == 0, 0, rpfit$isplit[temp, 1])
##         frame <- data.frame(row.names = rpfit$inode[, 1], var = tname[svar + 
##             1], n = rpfit$inode[, 5], wt = rpfit$dnode[, 3], 
##             dev = rpfit$dnode[, 1], yval = rpfit$dnode[, 4], 
##             complexity = rpfit$dnode[, 2], ncompete = pmax(0, 
##                 rpfit$inode[, 3] - 1), nsurrogate = rpfit$inode[, 
##                 4])
##     }
##     if (method.int == 3) {
##         numclass <- init$numresp - 2
##         nodeprob <- rpfit$dnode[, numclass + 5]/sum(wt)
##         temp <- pmax(1, init$counts)
##         temp <- rpfit$dnode[, 4 + (1:numclass)] %*% diag(init$parms$prior/temp)
##         yprob <- temp/rowSums(temp)
##         yval2 <- matrix(rpfit$dnode[, 4 + (0:numclass)], ncol = numclass + 
##             1)
##         frame$yval2 <- cbind(yval2, yprob, nodeprob)
##     }
##     else if (init$numresp > 1) 
##         frame$yval2 <- rpfit$dnode[, -(1:3), drop = FALSE]
##     if (is.null(init$summary)) 
##         stop("Initialization routine is missing the 'summary' function")
##     functions <- if (is.null(init$print)) 
##         list(summary = init$summary)
##     else list(summary = init$summary, print = init$print)
##     if (!is.null(init$text)) 
##         functions <- c(functions, list(text = init$text))
##     if (method == "user") 
##         functions <- c(functions, mlist)
##     where <- rpfit$which
##     names(where) <- row.names(m)
##     ans <- list(frame = frame, where = where, call = Call, terms = Terms, 
##         cptable = t(rpfit$cptable), method = method, parms = init$parms, 
##         control = controls, functions = functions, numresp = init$numresp)
##     if (nsplit) 
##         ans$splits = splits
##     if (ncat > 0) 
##         ans$csplit <- catmat + 2
##     if (nsplit) 
##         ans$variable.importance <- importance(ans)
##     if (model) {
##         ans$model <- m
##         if (missing(y)) 
##             y <- FALSE
##     }
##     if (y) 
##         ans$y <- Y
##     if (x) {
##         ans$x <- X
##         ans$wt <- wt
##     }
##     ans$ordered <- isord
##     if (!is.null(attr(m, "na.action"))) 
##         ans$na.action <- attr(m, "na.action")
##     if (!is.null(xlevels)) 
##         attr(ans, "xlevels") <- xlevels
##     if (method == "class") 
##         attr(ans, "ylevels") <- init$ylevels
##     class(ans) <- "rpart"
##     ans
## })(formula = .outcome ~ ., data = list(c(1, 2, 3, 4, 5, 6, 7, 
## 8, 9, 10, 11, 13, 14, 15, 16, 17, 18, 20, 21, 22, 26, 27, 28, 
## 29, 31, 33, 34, 36, 38, 39, 41, 42, 43, 44, 45, 46, 47, 48, 49, 
## 50, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 64, 65, 66, 67, 68, 
## 69, 70, 72, 73, 74, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 
## 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 
## 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 
## 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 127, 129, 
## 130, 131, 132, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143, 
## 144, 145, 146, 147, 148, 149, 150, 151, 152, 153, 154, 155, 157, 
## 159, 160, 162, 163, 164, 165, 166, 168, 170, 171, 172, 173, 175, 
## 176, 177, 178, 180, 181, 182, 183, 184, 185, 186, 188, 189, 190, 
## 191, 192, 193, 194, 195, 196, 197, 198, 199, 200, 201, 202, 203, 
## 204, 206, 207, 208, 209, 210, 211, 212, 213, 214, 216, 217, 218, 
## 220, 221, 222, 224, 225, 226, 227, 228, 229, 230, 231, 232, 233, 
## 234, 235, 236, 237, 238, 239, 240, 241, 242, 243, 244, 245, 246, 
## 247, 248, 250, 251, 253, 254, 255, 256, 257, 258, 259, 260, 261, 
## 262, 263, 264, 265, 266, 267, 268, 269, 270, 271, 272, 273, 274, 
## 276, 277, 278, 279, 280, 281, 282, 283, 284, 285, 286, 287, 288, 
## 289, 290, 292, 293, 294, 295, 297, 300, 301, 302, 303, 304, 305, 
## 306, 307, 308, 309, 310, 311, 312, 313, 314, 315, 316, 317, 318, 
## 319, 320, 322, 323, 324, 325, 326, 327, 328, 329, 330, 331, 332, 
## 333, 336, 337, 338, 339, 340, 341, 342, 343, 344, 345, 346, 347, 
## 348, 349, 350, 353, 354, 355, 356, 357, 358, 359, 360, 361, 362, 
## 363, 364, 365, 366, 367, 368, 369, 370, 371, 372, 373, 374, 375, 
## 376, 377, 378, 379, 380, 381, 383, 384, 385, 387, 388, 390, 391, 
## 392, 394, 396, 397, 398, 400, 401, 402, 403, 404, 405, 407, 409, 
## 410, 411, 412, 413, 414, 416, 417, 418, 419, 420, 421, 422, 423, 
## 424, 425, 426, 428, 429, 431, 432, 433, 434, 435, 437, 438, 440, 
## 441, 442, 443, 444, 446, 448, 449, 450, 451, 453, 454, 455, 456, 
## 457, 458, 459, 460, 461, 462, 463, 464, 465, 466, 467, 468, 469, 
## 470, 471, 473, 474, 475, 476, 477, 478, 479, 480, 481, 482, 483, 
## 484, 485, 486, 487, 488, 489, 490, 491, 492, 493, 494, 495, 496, 
## 497, 498, 499, 500, 501, 502, 503, 504, 505, 506, 507, 508, 510, 
## 511, 512, 513, 514, 515, 516, 517, 520, 521, 522, 524, 525, 527, 
## 528, 529, 532, 533, 534, 535, 536, 538, 539, 540, 543, 544, 545, 
## 546, 547, 548, 549, 550, 551, 553, 554, 555, 557, 558, 559, 560, 
## 561, 562, 563, 565, 567, 568, 569, 570, 572, 573, 574, 575, 576, 
## 577, 578, 581, 582, 583, 584, 585, 586, 587, 588, 590, 591, 592, 
## 594, 595, 596, 597, 599, 600, 602, 603, 604, 605, 606, 608, 609, 
## 610, 611, 612, 613, 615, 616, 617, 618, 619, 620, 621, 622, 623, 
## 624, 625, 626, 627, 628, 629, 630, 631, 632, 633, 634, 637, 639, 
## 640, 641, 642, 643, 644, 645, 646, 647, 650, 651, 653, 654, 655, 
## 656, 657, 658, 659, 660, 661, 662, 663, 664, 667, 668, 669, 670, 
## 671, 673, 674, 675, 676, 677, 678, 679, 680, 681, 682, 683, 684, 
## 686, 687, 688, 689, 690, 691, 692, 693, 694, 695, 696, 697, 698, 
## 700, 701, 705, 706, 707, 708, 709, 711, 712, 714, 715, 716, 717, 
## 718, 719, 720, 721, 722, 723, 724, 725, 726, 728, 729, 730, 731, 
## 732, 733, 734, 735, 736, 737, 738, 739, 740, 741, 742, 743, 745, 
## 746, 747, 749, 750, 751, 753, 755, 756, 757, 758, 759, 760, 761, 
## 762, 764, 765, 767, 768, 769, 770, 771, 772, 773, 774, 775, 776, 
## 777, 778, 779, 780, 781, 782), c(1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 
## 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 
## 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 
## 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 
## 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 
## 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 
## 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 
## 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 
## 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 
## 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 
## 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 
## 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 
## 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 
## 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 
## 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 
## 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 
## 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 
## 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 
## 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 
## 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 
## 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 
## 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 
## 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 
## 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 
## 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 
## 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 4, 4, 4, 4, 4, 4, 4, 4, 4, 
## 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 
## 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 
## 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 
## 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 
## 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 
## 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 
## 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4), c(19, 18, 19, 
## 18, 18, 18, 18, 19, 20, 19, 18, 19, 18, 18, 18, 18, 18, 18, 18, 
## 18, 18, 19, 18, 19, 20, 18, 19, 18, 18, 19, 19, 18, 19, 19, 18, 
## 18, 19, 18, 18, 18, 18, 18, 18, 18, 19, 19, 18, 19, 19, 19, 19, 
## 18, 18, 18, 20, 24, 19, 19, 20, 18, 20, 19, 19, 20, 20, 19, 18, 
## 18, 20, 20, 18, 19, 18, 18, 18, 19, 18, 19, 18, 20, 19, 18, 18, 
## 19, 18, 19, 18, 18, 18, 18, 19, 19, 18, 19, 19, 18, 18, 18, 18, 
## 18, 18, 18, 18, 18, 18, 18, 18, 18, 18, 18, 19, 19, 18, 18, 18, 
## 18, 20, 21, 18, 19, 19, 18, 19, 19, 19, 18, 18, 23, 19, 18, 19, 
## 18, 20, 18, 19, 19, 18, 18, 20, 18, 18, 18, 20, 19, 19, 18, 19, 
## 18, 19, 18, 20, 18, 18, 18, 18, 20, 18, 18, 19, 18, 19, 23, 21, 
## 20, 20, 26, 20, 19, 20, 19, 20, 19, 19, 19, 23, 19, 19, 19, 20, 
## 18, 19, 21, 20, 19, 19, 20, 20, 24, 20, 20, 20, 21, 20, 19, 19, 
## 18, 24, 19, 19, 19, 19, 21, 19, 19, 18, 19, 23, 21, 22, 20, 20, 
## 20, 19, 19, 19, 19, 21, 19, 19, 19, 20, 20, 18, 21, 19, 20, 19, 
## 20, 19, 19, 19, 19, 19, 21, 20, 19, 20, 24, 22, 19, 20, 21, 19, 
## 20, 21, 19, 21, 20, 25, 19, 20, 20, 19, 20, 23, 20, 20, 19, 20, 
## 20, 20, 19, 23, 20, 19, 19, 20, 19, 19, 19, 19, 19, 19, 18, 19, 
## 19, 19, 19, 19, 20, 19, 20, 24, 19, 19, 19, 20, 21, 22, 19, 19, 
## 23, 19, 19, 18, 20, 19, 19, 19, 20, 19, 20, 20, 19, 21, 19, 19, 
## 19, 22, 22, 19, 22, 20, 19, 19, 21, 22, 19, 20, 18, 20, 19, 20, 
## 20, 19, 19, 19, 21, 19, 18, 20, 21, 19, 19, 20, 19, 19, 19, 20, 
## 18, 22, 19, 19, 20, 19, 19, 19, 20, 19, 20, 19, 20, 20, 19, 20, 
## 20, 19, 21, 19, 23, 19, 19, 20, 20, 19, 24, 19, 21, 20, 19, 19, 
## 19, 19, 21, 19, 19, 20, 20, 19, 20, 19, 19, 19, 19, 19, 19, 23, 
## 19, 19, 19, 20, 20, 19, 19, 20, 19, 20, 20, 19, 25, 20, 21, 19, 
## 21, 20, 19, 20, 19, 20, 20, 20, 21, 20, 20, 20, 22, 20, 20, 20, 
## 21, 22, 20, 20, 21, 20, 20, 21, 22, 26, 20, 20, 20, 20, 20, 20, 
## 21, 21, 20, 20, 20, 20, 20, 21, 20, 21, 20, 28, 20, 21, 20, 21, 
## 20, 21, 22, 23, 21, 23, 19, 22, 21, 21, 20, 19, 20, 22, 21, 20, 
## 20, 20, 21, 20, 21, 20, 21, 21, 21, 22, 20, 22, 20, 20, 20, 21, 
## 23, 23, 22, 21, 21, 21, 21, 21, 21, 21, 22, 21, 22, 23, 22, 21, 
## 22, 22, 21, 22, 18, 21, 22, 22, 25, 21, 22, 21, 21, 21, 21, 21, 
## 23, 21, 21, 20, 27, 21, 22, 22, 22, 23, 21, 23, 22, 22, 21, 24, 
## 21, 22, 21, 21, 21, 21, 21, 21, 21, 21, 21, 21, 22, 21, 21, 21, 
## 21, 22, 22, 22, 21, 22, 24, 22, 21, 22, 21, 24, 21, 21, 21, 22, 
## 21, 22, 21, 21, 22, 22, 25, 21, 24, 22, 22, 21, 21, 21, 21, 22, 
## 22, 22, 22, 26, 21, 21, 22, 21, 21, 21, 22, 21, 22, 22, 21, 22, 
## 22, 21, 22, 20, 21, 21, 21, 21, 20, 23, 21, 22, 21, 21, 21, 21, 
## 21, 21, 21, 21, 21, 21, 22, 21, 21, 22, 21, 21, 21, 21, 23, 22, 
## 21, 21, 24, 22, 22, 22, 22, 22, 22, 26, 22, 22, 21, 21, 22, 22, 
## 30, 23, 22, 22, 22, 23, 24, 23, 22, 23, 22, 22, 24, 22, 22, 22, 
## 23, 24, 24, 23, 22, 22, 21, 22, 23, 22, 23, 22, 23, 22, 24, 22, 
## 22, 22), c(1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 
## 1, 1, 0, 1, 1, 0, 0, 0, 1, 0, 1, 1, 1, 1, 1, 0, 1, 0, 0, 0, 1, 
## 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 1, 0, 1, 0, 0, 1, 0, 
## 1, 1, 0, 0, 0, 1, 0, 1, 0, 0, 0, 1, 1, 0, 0, 1, 1, 0, 1, 0, 0, 
## 0, 1, 0, 0, 1, 1, 1, 0, 1, 0, 1, 1, 1, 0, 0, 1, 1, 0, 1, 1, 0, 
## 0, 0, 1, 1, 0, 1, 0, 0, 0, 0, 1, 0, 0, 1, 1, 1, 0, 1, 0, 0, 0, 
## 1, 0, 0, 1, 0, 0, 1, 0, 1, 0, 1, 0, 1, 0, 0, 0, 1, 0, 0, 1, 0, 
## 1, 0, 1, 0, 1, 0, 0, 1, 1, 1, 1, 0, 1, 1, 0, 1, 1, 0, 0, 0, 1, 
## 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 1, 1, 1, 1, 1, 
## 1, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 1, 0, 1, 0, 
## 1, 0, 0, 0, 0, 0, 0, 1, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 
## 1, 0, 1, 0, 0, 0, 1, 1, 1, 1, 1, 1, 0, 0, 0, 1, 0, 1, 0, 1, 1, 
## 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 1, 0, 1, 1, 
## 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 1, 1, 0, 
## 1, 1, 0, 1, 0, 1, 0, 0, 0, 1, 1, 0, 1, 1, 1, 1, 0, 0, 1, 0, 1, 
## 1, 1, 0, 1, 1, 1, 0, 1, 0, 1, 0, 0, 1, 1, 1, 0, 1, 0, 1, 0, 0, 
## 1, 1, 1, 0, 0, 1, 1, 0, 1, 1, 0, 0, 0, 0, 1, 0, 1, 1, 1, 0, 0, 
## 1, 0, 1, 1, 1, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 
## 1, 1, 1, 0, 0, 0, 0, 1, 1, 0, 1, 0, 0, 1, 0, 0, 1, 0, 0, 1, 1, 
## 1, 1, 0, 0, 0, 1, 1, 0, 1, 0, 1, 1, 0, 0, 0, 1, 1, 0, 0, 0, 0, 
## 1, 0, 0, 0, 0, 1, 1, 0, 0, 1, 1, 0, 1, 0, 0, 1, 1, 0, 1, 1, 1, 
## 1, 0, 1, 0, 0, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 0, 1, 0, 0, 1, 1, 
## 1, 1, 0, 1, 0, 1, 0, 1, 0, 0, 0, 1, 1, 0, 0, 1, 0, 1, 1, 0, 1, 
## 0, 0, 1, 0, 0, 1, 0, 0, 1, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 1, 1, 
## 1, 1, 0, 0, 1, 1, 0, 1, 0, 1, 0, 0, 1, 0, 1, 0, 0, 1, 1, 1, 1, 
## 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 1, 0, 1, 0, 1, 0, 
## 0, 0, 1, 1, 0, 1, 0, 0, 1, 0, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 1, 
## 0, 0, 0, 1, 0, 0, 1, 1, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 1, 0, 0, 
## 0, 0, 0, 0, 1, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 
## 0, 1, 1, 1, 1, 0, 0, 0, 1, 1, 1, 1, 1, 0, 0, 0, 1, 1, 0, 0, 0, 
## 1, 0, 1, 1, 1, 0, 0, 0, 1, 0, 1, 0, 0, 1, 1, 1, 1, 1, 1, 0, 0, 
## 0, 1, 1, 0, 0, 1, 1, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 1, 1, 0, 1, 
## 0, 0, 1, 1, 1, 0, 1, 1, 0), c(0, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1, 
## 0, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 
## 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 1, 1, 1, 1, 1, 1, 
## 0, 0, 1, 1, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 0, 
## 1, 1, 0, 1, 0, 1, 0, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1, 0, 
## 1, 1, 0, 0, 1, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 0, 
## 1, 1, 1, 0, 1, 0, 0, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 
## 1, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 0, 0, 1, 
## 1, 0, 1, 1, 0, 0, 1, 0, 0, 0, 0, 1, 1, 1, 0, 1, 0, 0, 0, 1, 0, 
## 0, 1, 1, 0, 1, 1, 1, 1, 0, 0, 0, 1, 0, 1, 1, 1, 1, 0, 0, 1, 1, 
## 1, 1, 1, 1, 0, 1, 1, 0, 1, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 0, 1, 
## 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 0, 1, 1, 0, 1, 0, 0, 1, 0, 
## 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 0, 1, 0, 1, 0, 0, 0, 1, 1, 
## 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 
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## 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0), c(1, 1, 1, 0, 1, 1, 1, 
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## 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 0, 1, 1, 1, 1, 
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## 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 
## 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 
## 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 
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## 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 
## 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 
## 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 
## 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 
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## 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 
## 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 
## 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 
## 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 
## 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 
## 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 
## 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 
## 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 1, 
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## 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 
## 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1), c(1, 
## 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 
## 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 
## 1, 0, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 
## 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 
## 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 
## 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 
## 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 
## 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 
## 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 
## 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 
## 1, 1, 1, 0, 1, 1, 0, 1, 1, 0, 0, 1, 0, 1, 1, 1, 1, 0, 1, 1, 0, 
## 1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 
## 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 
## 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 
## 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 
## 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 
## 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 
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## 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 
## 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 
## 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 
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## 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 
## 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 1, 
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## 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 1, 
## 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 
## 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 
## 1, 1, 1, 1), c(1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 
## 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 
## 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 
## 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 
## 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 
## 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 0, 1, 1, 1, 1, 
## 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 
## 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 
## 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 
## 1, 0, 1, 0, 0, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 
## 1, 0, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 0, 1, 1, 1, 0, 1, 0, 1, 1, 
## 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 
## 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 
## 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 
## 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 
## 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 
## 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 
## 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 
## 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 
## 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 
## 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 
## 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 
## 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 
## 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 
## 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 
## 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 
## 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 
## 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 
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## 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 
## 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 
## 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 0, 1, 1, 1, 
## 1, 1, 1, 1, 1, 1, 1, 1, 1, 1), c(0, 1, 1, 0, 0, 0, 0, 1, 1, 0, 
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## 1, 1, 0, 1, 1, 1, 0, 0, 0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 0, 1, 1, 
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## 1, 0, 1, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 
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## 1, 0, 0, 1, 1, 0, 0, 0, 1, 0, 0, 1, 1, 0, 0, 0, 0, 0, 1, 0, 0, 
## 1, 1, 0, 1, 1, 0, 1, 1, 0, 0, 0, 1, 1, 0, 1, 1, 0, 0, 0, 1, 0, 
## 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 1, 0, 1, 0, 1, 1, 0, 0, 1, 1, 
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## 1, 1, 1, 1, 1, 0, 0, 1, 1, 0, 1, 1, 1, 0, 1, 1, 0, 0, 0, 1, 1, 
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## 1, 0, 1, 1, 1, 1, 0, 0, 1, 0, 1, 0, 0, 1, 1, 0, 0, 1, 0, 1, 1, 
## 1, 0, 1, 1, 0, 1, 1, 0, 0, 0, 1, 1, 0, 0, 1, 1, 1, 1, 0, 1, 1, 
## 1, 0, 0, 1, 1, 0, 1, 1, 1, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 1, 1, 
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## 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 0, 1, 1, 0, 0, 0, 0, 
## 0, 0, 0, 1, 1, 0, 1, 0, 1, 1, 0, 0, 1, 0, 1, 1, 1, 0, 0, 1, 0, 
## 0, 1, 1, 0, 0, 1, 1, 1, 0, 1, 0, 0, 0, 1, 1, 0), c(1, 0, 0, 0, 
## 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 1, 0, 1, 0, 0, 1, 
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## 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 1, 0, 
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## 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 
## 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 
## 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 
## 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 
## 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 
## 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 
## 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 
## 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 
## 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 
## 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 
## 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 
## 1, 1, 0, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 
## 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 
## 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 
## 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 
## 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 
## 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 
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## 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1), c(1, 1, 1, 1, 
## 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 
## 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 
## 1, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 
## 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 
## 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 
## 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 
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## 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 
## 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 0, 1, 1, 1, 0, 
## 1, 1, 1, 1, 0, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 1, 
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## 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 
## 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 
## 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 
## 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 
## 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 
## 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 
## 1, 1, 1, 1, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 
## 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 
## 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 
## 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 
## 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 
## 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 
## 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 
## 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 
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## 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 
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## 1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 
## 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 
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## 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 
## 0, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 0, 
## 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 
## 1, 1, 1, 1, 1, 0, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 
## 1, 1, 1, 0), c(2, 2, 2, 1, 2, 2, 2, 2, 1, 2, 2, 2, 2, 1, 2, 2, 
## 2, 2, 2, 2, 2, 2, 1, 2, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1, 2, 1, 
## 2, 2, 2, 2, 2, 1, 2, 1, 2, 1, 2, 2, 2, 2, 2, 1, 2, 1, 2, 1, 2, 
## 1, 2, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1, 2, 2, 1, 1, 1, 2, 2, 
## 2, 2, 2, 2, 1, 2, 1, 2, 2, 2, 2, 2, 2, 2, 2, 1, 2, 2, 2, 1, 2, 
## 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1, 1, 1, 1, 2, 1, 1, 2, 1, 2, 
## 1, 2, 1, 1, 2, 2, 1, 1, 1, 2, 2, 2, 1, 2, 1, 1, 2, 1, 2, 2, 2, 
## 2, 1, 2, 1, 2, 2, 2, 2, 1, 2, 1, 1, 2, 2, 2, 2, 2, 2, 2, 1, 1, 
## 2, 1, 2, 2, 1, 2, 1, 1, 2, 2, 2, 2, 1, 1, 2, 1, 1, 2, 1, 2, 2, 
## 2, 2, 2, 2, 1, 2, 2, 1, 2, 2, 2, 2, 1, 2, 2, 1, 2, 2, 1, 2, 1, 
## 1, 1, 1, 1, 2, 1, 1, 2, 2, 1, 2, 2, 2, 2, 2, 2, 2, 2, 1, 2, 2, 
## 2, 2, 1, 1, 2, 1, 2, 1, 2, 2, 2, 2, 2, 2, 2, 2, 1, 2, 2, 2, 2, 
## 2, 2, 2, 2, 1, 2, 2, 1, 1, 1, 1, 2, 1, 2, 2, 2, 2, 1, 2, 1, 1, 
## 2, 1, 1, 1, 2, 2, 1, 1, 2, 2, 1, 1, 1, 2, 1, 2, 1, 2, 2, 1, 1, 
## 2, 1, 2, 1, 2, 2, 2, 2, 2, 2, 2, 2, 1, 2, 2, 1, 1, 1, 2, 1, 2, 
## 2, 1, 2, 2, 2, 2, 2, 2, 2, 2, 1, 1, 1, 2, 1, 2, 2, 2, 2, 2, 2, 
## 2, 2, 1, 2, 1, 2, 2, 2, 1, 2, 2, 2, 1, 1, 2, 2, 2, 2, 2, 2, 2, 
## 2, 1, 2, 2, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1, 1, 1, 2, 
## 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1, 2, 2, 2, 2, 1, 2, 1, 1, 1, 
## 2, 2, 1, 2, 2, 2, 2, 2, 2, 2, 2, 1, 2, 2, 2, 1, 2, 2, 2, 2, 1, 
## 1, 1, 2, 2, 2, 2, 1, 2, 1, 2, 1, 2, 1, 2, 2, 2, 2, 2, 2, 2, 2, 
## 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1, 2, 1, 1, 2, 2, 1, 2, 2, 2, 2, 
## 2, 1, 2, 1, 2, 2, 1, 2, 2, 2, 1, 1, 2, 2, 2, 2, 2, 2, 1, 2, 2, 
## 2, 1, 2, 1, 2, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1, 2, 
## 2, 1, 2, 2, 2, 2, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1, 2, 2, 2, 
## 2, 2, 1, 2, 2, 2, 2, 2, 2, 2, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 
## 1, 1, 2, 2, 2, 2, 1, 1, 2, 1, 2, 2, 2, 2, 1, 2, 1, 2, 1, 1, 2, 
## 2, 2, 2, 2, 1, 2, 2, 2, 2, 2, 2, 1, 2, 2, 2, 2, 2, 1, 1, 2, 2, 
## 2, 2, 2, 2, 2, 2, 1, 1, 2, 2, 1, 2, 2, 2, 1, 1, 2, 2, 2, 1, 2, 
## 2, 2, 1, 2, 2, 2, 2, 2, 1, 1, 2, 2, 1, 2, 1, 1, 2, 2, 1, 2, 1, 
## 2, 1, 1, 2, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 1, 1, 2, 2, 2, 2, 2, 
## 2, 1, 2, 2, 2, 2, 2, 2, 2, 2, 1, 1, 2, 2, 2, 2, 2, 1, 2, 2, 2, 
## 2, 2, 2, 2, 2, 2, 2, 2, 2, 1)), parms = list("information"), 
##     control = list(20, 7, 0, 4, 5, 2, 0, 30, 0))
##   n= 677 
## 
##          CP nsplit rel error
## 1 0.6701031      0 1.0000000
## 2 0.2164948      1 0.3298969
## 3 0.1030928      2 0.1134021
## 
## Variable importance
## depression_severityModerate             phq_score(9,14] 
##                          36                          36 
##                   suicidal0   depression_severitySevere 
##                          19                           3 
##            phq_score(19,27]        epworth_score(17,24] 
##                           3                           2 
##               bmi(39.9,100]    who_bmiClass III Obesity 
##                           1                           1 
## 
## Node number 1: 677 observations,    complexity param=0.6701031
##   predicted class=0  expected loss=0.2865583  P(node) =1
##     class counts:   194   483
##    probabilities: 0.287 0.713 
##   left son=2 (130 obs) right son=3 (547 obs)
##   Primary splits:
##       phq_score(9,14]                 < 0.5 to the right, improve=208.13400, (0 missing)
##       depression_severityModerate     < 0.5 to the right, improve=208.13400, (0 missing)
##       phq_score(4,9]                  < 0.5 to the left,  improve= 95.97735, (0 missing)
##       suicidal0                       < 0.5 to the left,  improve= 85.73113, (0 missing)
##       depression_severityNone-minimal < 0.5 to the left,  improve= 71.79485, (0 missing)
##   Surrogate splits:
##       depression_severityModerate < 0.5 to the right, agree=1.000, adj=1.000, (0 split)
##       bmi(39.9,100]               < 0.5 to the right, agree=0.811, adj=0.015, (0 split)
##       who_bmiClass III Obesity    < 0.5 to the right, agree=0.811, adj=0.015, (0 split)
## 
## Node number 2: 130 observations
##   predicted class=1  expected loss=0  P(node) =0.1920236
##     class counts:   130     0
##    probabilities: 1.000 0.000 
## 
## Node number 3: 547 observations,    complexity param=0.2164948
##   predicted class=0  expected loss=0.1170018  P(node) =0.8079764
##     class counts:    64   483
##    probabilities: 0.117 0.883 
##   left son=6 (42 obs) right son=7 (505 obs)
##   Primary splits:
##       suicidal0                            < 0.5 to the left,  improve=106.96590, (0 missing)
##       phq_score(14,19]                     < 0.5 to the right, improve=100.78320, (0 missing)
##       depression_severityModerately severe < 0.5 to the right, improve=100.78320, (0 missing)
##       anxiousness0                         < 0.5 to the left,  improve= 49.96506, (0 missing)
##       gad_score(14,21]                     < 0.5 to the right, improve= 44.23279, (0 missing)
##   Surrogate splits:
##       phq_score(19,27]          < 0.5 to the right, agree=0.934, adj=0.143, (0 split)
##       depression_severitySevere < 0.5 to the right, agree=0.934, adj=0.143, (0 split)
##       epworth_score(17,24]      < 0.5 to the right, agree=0.931, adj=0.095, (0 split)
##       bmi(34.9,39.9]            < 0.5 to the right, agree=0.925, adj=0.024, (0 split)
##       who_bmiClass II Obesity   < 0.5 to the right, agree=0.925, adj=0.024, (0 split)
## 
## Node number 6: 42 observations
##   predicted class=1  expected loss=0  P(node) =0.0620384
##     class counts:    42     0
##    probabilities: 1.000 0.000 
## 
## Node number 7: 505 observations
##   predicted class=0  expected loss=0.04356436  P(node) =0.745938
##     class counts:    22   483
##    probabilities: 0.044 0.956

#accuracy of the tree 
accuracy <- tree1$results$Accuracy
print(paste("Accuracy:", accuracy))

## [1] "Accuracy: 0.956061959880227" "Accuracy: 0.939064599518454"
## [3] "Accuracy: 0.787680324123019"

# get the average accuracy across all fold
accuracy <- mean(tree1$results$Accuracy)
print(paste("Average Accuracy:", accuracy))

## [1] "Average Accuracy: 0.8942689611739"

tree1 <- valuation_df <- data.frame(tree = "tree1", accuracy = accuracy, Atrribute="information gain",
                                       cv_K=5)
evaluation_df <- rbind(evaluation_df, tree1)

# visulize the tree'
fancyRpartPlot(trained_model, caption = NULL)

Gini index

folds <- createFolds(dataset$depressiveness, k = 5)  # create the folds
# control parameters 
train_control <- trainControl(method = "cv", index = folds)

#build the model
tree <- train(depressiveness ~ ., data = dataset, trControl = train_control, 
               method = "rpart")

# train result
trained_model <- tree$finalModel
trained_model

## n= 677 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 677 194 0 (0.2865583 0.7134417)  
##   2) phq_score(9,14]>=0.5 130   0 1 (1.0000000 0.0000000) *
##   3) phq_score(9,14]< 0.5 547  64 0 (0.1170018 0.8829982) *

trained_model$variable.importance

## depression_severityModerate             phq_score(9,14] 
##                  163.791596                  163.791596 
##               bmi(39.9,100]    who_bmiClass III Obesity 
##                    2.519871                    2.519871

#summary of the trainig 
summary(trained_model)

## Call:
## (function (formula, data, weights, subset, na.action = na.rpart, 
##     method, model = FALSE, x = FALSE, y = TRUE, parms, control, 
##     cost, ...) 
## {
##     Call <- match.call()
##     if (is.data.frame(model)) {
##         m <- model
##         model <- FALSE
##     }
##     else {
##         indx <- match(c("formula", "data", "weights", "subset"), 
##             names(Call), nomatch = 0)
##         if (indx[1] == 0) 
##             stop("a 'formula' argument is required")
##         temp <- Call[c(1, indx)]
##         temp$na.action <- na.action
##         temp[[1]] <- quote(stats::model.frame)
##         m <- eval.parent(temp)
##     }
##     Terms <- attr(m, "terms")
##     if (any(attr(Terms, "order") > 1)) 
##         stop("Trees cannot handle interaction terms")
##     Y <- model.response(m)
##     wt <- model.weights(m)
##     if (any(wt < 0)) 
##         stop("negative weights not allowed")
##     if (!length(wt)) 
##         wt <- rep(1, nrow(m))
##     offset <- model.offset(m)
##     X <- rpart.matrix(m)
##     nobs <- nrow(X)
##     nvar <- ncol(X)
##     if (missing(method)) {
##         method <- if (is.factor(Y) || is.character(Y)) 
##             "class"
##         else if (inherits(Y, "Surv")) 
##             "exp"
##         else if (is.matrix(Y)) 
##             "poisson"
##         else "anova"
##     }
##     if (is.list(method)) {
##         mlist <- method
##         method <- "user"
##         init <- if (missing(parms)) 
##             mlist$init(Y, offset, wt = wt)
##         else mlist$init(Y, offset, parms, wt)
##         keep <- rpartcallback(mlist, nobs, init)
##         method.int <- 4
##         parms <- init$parms
##     }
##     else {
##         method.int <- pmatch(method, c("anova", "poisson", "class", 
##             "exp"))
##         if (is.na(method.int)) 
##             stop("Invalid method")
##         method <- c("anova", "poisson", "class", "exp")[method.int]
##         if (method.int == 4) 
##             method.int <- 2
##         init <- if (missing(parms)) 
##             get(paste("rpart", method, sep = "."), envir = environment())(Y, 
##                 offset, , wt)
##         else get(paste("rpart", method, sep = "."), envir = environment())(Y, 
##             offset, parms, wt)
##         ns <- asNamespace("rpart")
##         if (!is.null(init$print)) 
##             environment(init$print) <- ns
##         if (!is.null(init$summary)) 
##             environment(init$summary) <- ns
##         if (!is.null(init$text)) 
##             environment(init$text) <- ns
##     }
##     Y <- init$y
##     xlevels <- .getXlevels(Terms, m)
##     cats <- rep(0, ncol(X))
##     if (!is.null(xlevels)) {
##         indx <- match(names(xlevels), colnames(X), nomatch = 0)
##         cats[indx] <- (unlist(lapply(xlevels, length)))[indx > 
##             0]
##     }
##     extraArgs <- list(...)
##     if (length(extraArgs)) {
##         controlargs <- names(formals(rpart.control))
##         indx <- match(names(extraArgs), controlargs, nomatch = 0)
##         if (any(indx == 0)) 
##             stop(gettextf("Argument %s not matched", names(extraArgs)[indx == 
##                 0]), domain = NA)
##     }
##     controls <- rpart.control(...)
##     if (!missing(control)) 
##         controls[names(control)] <- control
##     xval <- controls$xval
##     if (is.null(xval) || (length(xval) == 1 && xval == 0) || 
##         method == "user") {
##         xgroups <- 0
##         xval <- 0
##     }
##     else if (length(xval) == 1) {
##         xgroups <- sample(rep(1:xval, length.out = nobs), nobs, 
##             replace = FALSE)
##     }
##     else if (length(xval) == nobs) {
##         xgroups <- xval
##         xval <- length(unique(xgroups))
##     }
##     else {
##         if (!is.null(attr(m, "na.action"))) {
##             temp <- as.integer(attr(m, "na.action"))
##             xval <- xval[-temp]
##             if (length(xval) == nobs) {
##                 xgroups <- xval
##                 xval <- length(unique(xgroups))
##             }
##             else stop("Wrong length for 'xval'")
##         }
##         else stop("Wrong length for 'xval'")
##     }
##     if (missing(cost)) 
##         cost <- rep(1, nvar)
##     else {
##         if (length(cost) != nvar) 
##             stop("Cost vector is the wrong length")
##         if (any(cost <= 0)) 
##             stop("Cost vector must be positive")
##     }
##     tfun <- function(x) if (is.matrix(x)) 
##         rep(is.ordered(x), ncol(x))
##     else is.ordered(x)
##     labs <- sub("^`(.*)`$", "\\1", attr(Terms, "term.labels"))
##     isord <- unlist(lapply(m[labs], tfun))
##     storage.mode(X) <- "double"
##     storage.mode(wt) <- "double"
##     temp <- as.double(unlist(init$parms))
##     if (!length(temp)) 
##         temp <- 0
##     rpfit <- .Call(C_rpart, ncat = as.integer(cats * !isord), 
##         method = as.integer(method.int), as.double(unlist(controls)), 
##         temp, as.integer(xval), as.integer(xgroups), as.double(t(init$y)), 
##         X, wt, as.integer(init$numy), as.double(cost))
##     nsplit <- nrow(rpfit$isplit)
##     ncat <- if (!is.null(rpfit$csplit)) 
##         nrow(rpfit$csplit)
##     else 0
##     if (nsplit == 0) 
##         xval <- 0
##     numcp <- ncol(rpfit$cptable)
##     temp <- if (nrow(rpfit$cptable) == 3) 
##         c("CP", "nsplit", "rel error")
##     else c("CP", "nsplit", "rel error", "xerror", "xstd")
##     dimnames(rpfit$cptable) <- list(temp, 1:numcp)
##     tname <- c("<leaf>", colnames(X))
##     splits <- matrix(c(rpfit$isplit[, 2:3], rpfit$dsplit), ncol = 5, 
##         dimnames = list(tname[rpfit$isplit[, 1] + 1], c("count", 
##             "ncat", "improve", "index", "adj")))
##     index <- rpfit$inode[, 2]
##     nadd <- sum(isord[rpfit$isplit[, 1]])
##     if (nadd > 0) {
##         newc <- matrix(0, nadd, max(cats))
##         cvar <- rpfit$isplit[, 1]
##         indx <- isord[cvar]
##         cdir <- splits[indx, 2]
##         ccut <- floor(splits[indx, 4])
##         splits[indx, 2] <- cats[cvar[indx]]
##         splits[indx, 4] <- ncat + 1:nadd
##         for (i in 1:nadd) {
##             newc[i, 1:(cats[(cvar[indx])[i]])] <- -as.integer(cdir[i])
##             newc[i, 1:ccut[i]] <- as.integer(cdir[i])
##         }
##         catmat <- if (ncat == 0) 
##             newc
##         else {
##             cs <- rpfit$csplit
##             ncs <- ncol(cs)
##             ncc <- ncol(newc)
##             if (ncs < ncc) 
##                 cs <- cbind(cs, matrix(0, nrow(cs), ncc - ncs))
##             rbind(cs, newc)
##         }
##         ncat <- ncat + nadd
##     }
##     else catmat <- rpfit$csplit
##     if (nsplit == 0) {
##         frame <- data.frame(row.names = 1, var = "<leaf>", n = rpfit$inode[, 
##             5], wt = rpfit$dnode[, 3], dev = rpfit$dnode[, 1], 
##             yval = rpfit$dnode[, 4], complexity = rpfit$dnode[, 
##                 2], ncompete = 0, nsurrogate = 0)
##     }
##     else {
##         temp <- ifelse(index == 0, 1, index)
##         svar <- ifelse(index == 0, 0, rpfit$isplit[temp, 1])
##         frame <- data.frame(row.names = rpfit$inode[, 1], var = tname[svar + 
##             1], n = rpfit$inode[, 5], wt = rpfit$dnode[, 3], 
##             dev = rpfit$dnode[, 1], yval = rpfit$dnode[, 4], 
##             complexity = rpfit$dnode[, 2], ncompete = pmax(0, 
##                 rpfit$inode[, 3] - 1), nsurrogate = rpfit$inode[, 
##                 4])
##     }
##     if (method.int == 3) {
##         numclass <- init$numresp - 2
##         nodeprob <- rpfit$dnode[, numclass + 5]/sum(wt)
##         temp <- pmax(1, init$counts)
##         temp <- rpfit$dnode[, 4 + (1:numclass)] %*% diag(init$parms$prior/temp)
##         yprob <- temp/rowSums(temp)
##         yval2 <- matrix(rpfit$dnode[, 4 + (0:numclass)], ncol = numclass + 
##             1)
##         frame$yval2 <- cbind(yval2, yprob, nodeprob)
##     }
##     else if (init$numresp > 1) 
##         frame$yval2 <- rpfit$dnode[, -(1:3), drop = FALSE]
##     if (is.null(init$summary)) 
##         stop("Initialization routine is missing the 'summary' function")
##     functions <- if (is.null(init$print)) 
##         list(summary = init$summary)
##     else list(summary = init$summary, print = init$print)
##     if (!is.null(init$text)) 
##         functions <- c(functions, list(text = init$text))
##     if (method == "user") 
##         functions <- c(functions, mlist)
##     where <- rpfit$which
##     names(where) <- row.names(m)
##     ans <- list(frame = frame, where = where, call = Call, terms = Terms, 
##         cptable = t(rpfit$cptable), method = method, parms = init$parms, 
##         control = controls, functions = functions, numresp = init$numresp)
##     if (nsplit) 
##         ans$splits = splits
##     if (ncat > 0) 
##         ans$csplit <- catmat + 2
##     if (nsplit) 
##         ans$variable.importance <- importance(ans)
##     if (model) {
##         ans$model <- m
##         if (missing(y)) 
##             y <- FALSE
##     }
##     if (y) 
##         ans$y <- Y
##     if (x) {
##         ans$x <- X
##         ans$wt <- wt
##     }
##     ans$ordered <- isord
##     if (!is.null(attr(m, "na.action"))) 
##         ans$na.action <- attr(m, "na.action")
##     if (!is.null(xlevels)) 
##         attr(ans, "xlevels") <- xlevels
##     if (method == "class") 
##         attr(ans, "ylevels") <- init$ylevels
##     class(ans) <- "rpart"
##     ans
## })(formula = .outcome ~ ., data = list(c(1, 2, 3, 4, 5, 6, 7, 
## 8, 9, 10, 11, 13, 14, 15, 16, 17, 18, 20, 21, 22, 26, 27, 28, 
## 29, 31, 33, 34, 36, 38, 39, 41, 42, 43, 44, 45, 46, 47, 48, 49, 
## 50, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 64, 65, 66, 67, 68, 
## 69, 70, 72, 73, 74, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 
## 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 
## 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 
## 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 127, 129, 
## 130, 131, 132, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143, 
## 144, 145, 146, 147, 148, 149, 150, 151, 152, 153, 154, 155, 157, 
## 159, 160, 162, 163, 164, 165, 166, 168, 170, 171, 172, 173, 175, 
## 176, 177, 178, 180, 181, 182, 183, 184, 185, 186, 188, 189, 190, 
## 191, 192, 193, 194, 195, 196, 197, 198, 199, 200, 201, 202, 203, 
## 204, 206, 207, 208, 209, 210, 211, 212, 213, 214, 216, 217, 218, 
## 220, 221, 222, 224, 225, 226, 227, 228, 229, 230, 231, 232, 233, 
## 234, 235, 236, 237, 238, 239, 240, 241, 242, 243, 244, 245, 246, 
## 247, 248, 250, 251, 253, 254, 255, 256, 257, 258, 259, 260, 261, 
## 262, 263, 264, 265, 266, 267, 268, 269, 270, 271, 272, 273, 274, 
## 276, 277, 278, 279, 280, 281, 282, 283, 284, 285, 286, 287, 288, 
## 289, 290, 292, 293, 294, 295, 297, 300, 301, 302, 303, 304, 305, 
## 306, 307, 308, 309, 310, 311, 312, 313, 314, 315, 316, 317, 318, 
## 319, 320, 322, 323, 324, 325, 326, 327, 328, 329, 330, 331, 332, 
## 333, 336, 337, 338, 339, 340, 341, 342, 343, 344, 345, 346, 347, 
## 348, 349, 350, 353, 354, 355, 356, 357, 358, 359, 360, 361, 362, 
## 363, 364, 365, 366, 367, 368, 369, 370, 371, 372, 373, 374, 375, 
## 376, 377, 378, 379, 380, 381, 383, 384, 385, 387, 388, 390, 391, 
## 392, 394, 396, 397, 398, 400, 401, 402, 403, 404, 405, 407, 409, 
## 410, 411, 412, 413, 414, 416, 417, 418, 419, 420, 421, 422, 423, 
## 424, 425, 426, 428, 429, 431, 432, 433, 434, 435, 437, 438, 440, 
## 441, 442, 443, 444, 446, 448, 449, 450, 451, 453, 454, 455, 456, 
## 457, 458, 459, 460, 461, 462, 463, 464, 465, 466, 467, 468, 469, 
## 470, 471, 473, 474, 475, 476, 477, 478, 479, 480, 481, 482, 483, 
## 484, 485, 486, 487, 488, 489, 490, 491, 492, 493, 494, 495, 496, 
## 497, 498, 499, 500, 501, 502, 503, 504, 505, 506, 507, 508, 510, 
## 511, 512, 513, 514, 515, 516, 517, 520, 521, 522, 524, 525, 527, 
## 528, 529, 532, 533, 534, 535, 536, 538, 539, 540, 543, 544, 545, 
## 546, 547, 548, 549, 550, 551, 553, 554, 555, 557, 558, 559, 560, 
## 561, 562, 563, 565, 567, 568, 569, 570, 572, 573, 574, 575, 576, 
## 577, 578, 581, 582, 583, 584, 585, 586, 587, 588, 590, 591, 592, 
## 594, 595, 596, 597, 599, 600, 602, 603, 604, 605, 606, 608, 609, 
## 610, 611, 612, 613, 615, 616, 617, 618, 619, 620, 621, 622, 623, 
## 624, 625, 626, 627, 628, 629, 630, 631, 632, 633, 634, 637, 639, 
## 640, 641, 642, 643, 644, 645, 646, 647, 650, 651, 653, 654, 655, 
## 656, 657, 658, 659, 660, 661, 662, 663, 664, 667, 668, 669, 670, 
## 671, 673, 674, 675, 676, 677, 678, 679, 680, 681, 682, 683, 684, 
## 686, 687, 688, 689, 690, 691, 692, 693, 694, 695, 696, 697, 698, 
## 700, 701, 705, 706, 707, 708, 709, 711, 712, 714, 715, 716, 717, 
## 718, 719, 720, 721, 722, 723, 724, 725, 726, 728, 729, 730, 731, 
## 732, 733, 734, 735, 736, 737, 738, 739, 740, 741, 742, 743, 745, 
## 746, 747, 749, 750, 751, 753, 755, 756, 757, 758, 759, 760, 761, 
## 762, 764, 765, 767, 768, 769, 770, 771, 772, 773, 774, 775, 776, 
## 777, 778, 779, 780, 781, 782), c(1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 
## 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 
## 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 
## 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 
## 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 
## 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 
## 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 
## 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 
## 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 
## 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 
## 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 
## 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 
## 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 
## 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 
## 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 
## 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 
## 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 
## 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 
## 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 
## 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 
## 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 
## 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 
## 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 
## 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 
## 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 
## 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 4, 4, 4, 4, 4, 4, 4, 4, 4, 
## 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 
## 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 
## 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 
## 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 
## 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 
## 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 
## 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4), c(19, 18, 19, 
## 18, 18, 18, 18, 19, 20, 19, 18, 19, 18, 18, 18, 18, 18, 18, 18, 
## 18, 18, 19, 18, 19, 20, 18, 19, 18, 18, 19, 19, 18, 19, 19, 18, 
## 18, 19, 18, 18, 18, 18, 18, 18, 18, 19, 19, 18, 19, 19, 19, 19, 
## 18, 18, 18, 20, 24, 19, 19, 20, 18, 20, 19, 19, 20, 20, 19, 18, 
## 18, 20, 20, 18, 19, 18, 18, 18, 19, 18, 19, 18, 20, 19, 18, 18, 
## 19, 18, 19, 18, 18, 18, 18, 19, 19, 18, 19, 19, 18, 18, 18, 18, 
## 18, 18, 18, 18, 18, 18, 18, 18, 18, 18, 18, 19, 19, 18, 18, 18, 
## 18, 20, 21, 18, 19, 19, 18, 19, 19, 19, 18, 18, 23, 19, 18, 19, 
## 18, 20, 18, 19, 19, 18, 18, 20, 18, 18, 18, 20, 19, 19, 18, 19, 
## 18, 19, 18, 20, 18, 18, 18, 18, 20, 18, 18, 19, 18, 19, 23, 21, 
## 20, 20, 26, 20, 19, 20, 19, 20, 19, 19, 19, 23, 19, 19, 19, 20, 
## 18, 19, 21, 20, 19, 19, 20, 20, 24, 20, 20, 20, 21, 20, 19, 19, 
## 18, 24, 19, 19, 19, 19, 21, 19, 19, 18, 19, 23, 21, 22, 20, 20, 
## 20, 19, 19, 19, 19, 21, 19, 19, 19, 20, 20, 18, 21, 19, 20, 19, 
## 20, 19, 19, 19, 19, 19, 21, 20, 19, 20, 24, 22, 19, 20, 21, 19, 
## 20, 21, 19, 21, 20, 25, 19, 20, 20, 19, 20, 23, 20, 20, 19, 20, 
## 20, 20, 19, 23, 20, 19, 19, 20, 19, 19, 19, 19, 19, 19, 18, 19, 
## 19, 19, 19, 19, 20, 19, 20, 24, 19, 19, 19, 20, 21, 22, 19, 19, 
## 23, 19, 19, 18, 20, 19, 19, 19, 20, 19, 20, 20, 19, 21, 19, 19, 
## 19, 22, 22, 19, 22, 20, 19, 19, 21, 22, 19, 20, 18, 20, 19, 20, 
## 20, 19, 19, 19, 21, 19, 18, 20, 21, 19, 19, 20, 19, 19, 19, 20, 
## 18, 22, 19, 19, 20, 19, 19, 19, 20, 19, 20, 19, 20, 20, 19, 20, 
## 20, 19, 21, 19, 23, 19, 19, 20, 20, 19, 24, 19, 21, 20, 19, 19, 
## 19, 19, 21, 19, 19, 20, 20, 19, 20, 19, 19, 19, 19, 19, 19, 23, 
## 19, 19, 19, 20, 20, 19, 19, 20, 19, 20, 20, 19, 25, 20, 21, 19, 
## 21, 20, 19, 20, 19, 20, 20, 20, 21, 20, 20, 20, 22, 20, 20, 20, 
## 21, 22, 20, 20, 21, 20, 20, 21, 22, 26, 20, 20, 20, 20, 20, 20, 
## 21, 21, 20, 20, 20, 20, 20, 21, 20, 21, 20, 28, 20, 21, 20, 21, 
## 20, 21, 22, 23, 21, 23, 19, 22, 21, 21, 20, 19, 20, 22, 21, 20, 
## 20, 20, 21, 20, 21, 20, 21, 21, 21, 22, 20, 22, 20, 20, 20, 21, 
## 23, 23, 22, 21, 21, 21, 21, 21, 21, 21, 22, 21, 22, 23, 22, 21, 
## 22, 22, 21, 22, 18, 21, 22, 22, 25, 21, 22, 21, 21, 21, 21, 21, 
## 23, 21, 21, 20, 27, 21, 22, 22, 22, 23, 21, 23, 22, 22, 21, 24, 
## 21, 22, 21, 21, 21, 21, 21, 21, 21, 21, 21, 21, 22, 21, 21, 21, 
## 21, 22, 22, 22, 21, 22, 24, 22, 21, 22, 21, 24, 21, 21, 21, 22, 
## 21, 22, 21, 21, 22, 22, 25, 21, 24, 22, 22, 21, 21, 21, 21, 22, 
## 22, 22, 22, 26, 21, 21, 22, 21, 21, 21, 22, 21, 22, 22, 21, 22, 
## 22, 21, 22, 20, 21, 21, 21, 21, 20, 23, 21, 22, 21, 21, 21, 21, 
## 21, 21, 21, 21, 21, 21, 22, 21, 21, 22, 21, 21, 21, 21, 23, 22, 
## 21, 21, 24, 22, 22, 22, 22, 22, 22, 26, 22, 22, 21, 21, 22, 22, 
## 30, 23, 22, 22, 22, 23, 24, 23, 22, 23, 22, 22, 24, 22, 22, 22, 
## 23, 24, 24, 23, 22, 22, 21, 22, 23, 22, 23, 22, 23, 22, 24, 22, 
## 22, 22), c(1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 
## 1, 1, 0, 1, 1, 0, 0, 0, 1, 0, 1, 1, 1, 1, 1, 0, 1, 0, 0, 0, 1, 
## 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 1, 0, 1, 0, 0, 1, 0, 
## 1, 1, 0, 0, 0, 1, 0, 1, 0, 0, 0, 1, 1, 0, 0, 1, 1, 0, 1, 0, 0, 
## 0, 1, 0, 0, 1, 1, 1, 0, 1, 0, 1, 1, 1, 0, 0, 1, 1, 0, 1, 1, 0, 
## 0, 0, 1, 1, 0, 1, 0, 0, 0, 0, 1, 0, 0, 1, 1, 1, 0, 1, 0, 0, 0, 
## 1, 0, 0, 1, 0, 0, 1, 0, 1, 0, 1, 0, 1, 0, 0, 0, 1, 0, 0, 1, 0, 
## 1, 0, 1, 0, 1, 0, 0, 1, 1, 1, 1, 0, 1, 1, 0, 1, 1, 0, 0, 0, 1, 
## 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 1, 1, 1, 1, 1, 
## 1, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 1, 0, 1, 0, 
## 1, 0, 0, 0, 0, 0, 0, 1, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 
## 1, 0, 1, 0, 0, 0, 1, 1, 1, 1, 1, 1, 0, 0, 0, 1, 0, 1, 0, 1, 1, 
## 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 1, 0, 1, 1, 
## 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 1, 1, 0, 
## 1, 1, 0, 1, 0, 1, 0, 0, 0, 1, 1, 0, 1, 1, 1, 1, 0, 0, 1, 0, 1, 
## 1, 1, 0, 1, 1, 1, 0, 1, 0, 1, 0, 0, 1, 1, 1, 0, 1, 0, 1, 0, 0, 
## 1, 1, 1, 0, 0, 1, 1, 0, 1, 1, 0, 0, 0, 0, 1, 0, 1, 1, 1, 0, 0, 
## 1, 0, 1, 1, 1, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 
## 1, 1, 1, 0, 0, 0, 0, 1, 1, 0, 1, 0, 0, 1, 0, 0, 1, 0, 0, 1, 1, 
## 1, 1, 0, 0, 0, 1, 1, 0, 1, 0, 1, 1, 0, 0, 0, 1, 1, 0, 0, 0, 0, 
## 1, 0, 0, 0, 0, 1, 1, 0, 0, 1, 1, 0, 1, 0, 0, 1, 1, 0, 1, 1, 1, 
## 1, 0, 1, 0, 0, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 0, 1, 0, 0, 1, 1, 
## 1, 1, 0, 1, 0, 1, 0, 1, 0, 0, 0, 1, 1, 0, 0, 1, 0, 1, 1, 0, 1, 
## 0, 0, 1, 0, 0, 1, 0, 0, 1, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 1, 1, 
## 1, 1, 0, 0, 1, 1, 0, 1, 0, 1, 0, 0, 1, 0, 1, 0, 0, 1, 1, 1, 1, 
## 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 1, 0, 1, 0, 1, 0, 
## 0, 0, 1, 1, 0, 1, 0, 0, 1, 0, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 1, 
## 0, 0, 0, 1, 0, 0, 1, 1, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 1, 0, 0, 
## 0, 0, 0, 0, 1, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 
## 0, 1, 1, 1, 1, 0, 0, 0, 1, 1, 1, 1, 1, 0, 0, 0, 1, 1, 0, 0, 0, 
## 1, 0, 1, 1, 1, 0, 0, 0, 1, 0, 1, 0, 0, 1, 1, 1, 1, 1, 1, 0, 0, 
## 0, 1, 1, 0, 0, 1, 1, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 1, 1, 0, 1, 
## 0, 0, 1, 1, 1, 0, 1, 1, 0), c(0, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1, 
## 0, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 
## 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 1, 1, 1, 1, 1, 1, 
## 0, 0, 1, 1, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 0, 
## 1, 1, 0, 1, 0, 1, 0, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1, 0, 
## 1, 1, 0, 0, 1, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 0, 
## 1, 1, 1, 0, 1, 0, 0, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 
## 1, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 0, 0, 1, 
## 1, 0, 1, 1, 0, 0, 1, 0, 0, 0, 0, 1, 1, 1, 0, 1, 0, 0, 0, 1, 0, 
## 0, 1, 1, 0, 1, 1, 1, 1, 0, 0, 0, 1, 0, 1, 1, 1, 1, 0, 0, 1, 1, 
## 1, 1, 1, 1, 0, 1, 1, 0, 1, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 0, 1, 
## 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 0, 1, 1, 0, 1, 0, 0, 1, 0, 
## 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 0, 1, 0, 1, 0, 0, 0, 1, 1, 
## 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 
## 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 0, 0, 
## 1, 0, 1, 0, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1, 0, 1, 1, 0, 1, 
## 1, 0, 1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 
## 1, 0, 1, 0, 1, 0, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 0, 0, 0, 
## 1, 0, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 
## 1, 1, 1, 0, 0, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 1, 
## 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 0, 0, 1, 0, 1, 0, 1, 1, 0, 1, 
## 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 0, 
## 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 0, 1, 1, 0, 0, 1, 1, 0, 
## 1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 0, 1, 1, 0, 0, 0, 1, 0, 1, 1, 
## 0, 0, 0, 1, 0, 0, 1, 1, 1, 0, 1, 1, 1, 0, 1, 0, 1, 1, 0, 0, 0, 
## 0, 1, 1, 0, 0, 1, 1, 0, 1, 0, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 
## 1, 1, 0, 0, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 
## 1, 0, 1, 1, 1, 0, 0, 0, 1, 0, 1, 1, 1, 0, 0, 1, 0, 1, 0, 0, 1, 
## 1, 0, 0, 0, 1, 1, 1, 0, 1, 1, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 
## 1, 0, 1, 1, 1, 0, 1, 1, 1, 0, 0, 1, 0, 0, 0, 0, 1, 1, 0, 0, 1, 
## 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 
## 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 0, 0, 0, 1, 1, 0, 0, 0, 1, 0, 0, 
## 0, 0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 1, 0, 1, 1), c(0, 0, 1, 0, 1, 
## 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 1, 0, 1, 0, 
## 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 
## 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 
## 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 1, 0, 0, 0, 1, 0, 0, 1, 0, 0, 1, 
## 0, 0, 1, 0, 0, 1, 0, 0, 0, 1, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 
## 0, 0, 1, 0, 0, 1, 0, 0, 0, 1, 0, 1, 1, 0, 0, 1, 0, 1, 0, 0, 0, 
## 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
## 0, 1, 0, 1, 1, 0, 0, 1, 0, 0, 1, 0, 0, 0, 1, 1, 1, 0, 0, 0, 1, 
## 0, 1, 1, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 
## 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 1, 1, 1, 0, 0, 
## 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 
## 1, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 1, 
## 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 
## 0, 0, 0, 1, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
## 0, 1, 0, 0, 1, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 
## 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 
## 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 
## 0, 1, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 
## 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 
## 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 1, 0, 1, 
## 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 
## 1, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 
## 0, 1, 1, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 1, 
## 1, 1, 0, 1, 0, 0, 1, 1, 1, 0, 1, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 
## 1, 0, 0, 1, 1, 1, 1, 0, 0, 1, 1, 0, 0, 1, 0, 1, 1, 0, 0, 0, 0, 
## 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 
## 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 1, 0, 1, 0, 0, 0, 1, 1, 
## 0, 1, 0, 1, 1, 0, 0, 1, 1, 1, 0, 0, 0, 1, 0, 0, 1, 0, 1, 1, 0, 
## 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 1, 0, 1, 1, 1, 
## 1, 0, 0, 1, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 
## 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 1, 1, 1, 0, 0, 
## 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 1, 1, 1, 0, 1, 0, 0
## ), c(1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
## 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
## 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 
## 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 
## 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 
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## 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 
## 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1), c(1, 
## 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 
## 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 
## 1, 0, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 
## 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 
## 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 
## 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 
## 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 
## 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 
## 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 
## 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 
## 1, 1, 1, 0, 1, 1, 0, 1, 1, 0, 0, 1, 0, 1, 1, 1, 1, 0, 1, 1, 0, 
## 1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 
## 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 
## 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 
## 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 
## 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 
## 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 
## 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 
## 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 
## 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 
## 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 
## 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 
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## 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 
## 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 1, 
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## 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 
## 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 
## 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 1, 
## 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 
## 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 
## 1, 1, 1, 1), c(1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 
## 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 
## 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 
## 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 
## 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 
## 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 0, 1, 1, 1, 1, 
## 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 
## 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 
## 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 
## 1, 0, 1, 0, 0, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 
## 1, 0, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 0, 1, 1, 1, 0, 1, 0, 1, 1, 
## 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 
## 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 
## 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 
## 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 
## 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 
## 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 
## 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 
## 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 
## 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 
## 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 
## 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 
## 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 
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## 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 
## 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 
## 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 
## 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 
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## 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 
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## 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 0, 1, 1, 1, 
## 1, 1, 1, 1, 1, 1, 1, 1, 1, 1), c(0, 1, 1, 0, 0, 0, 0, 1, 1, 0, 
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## 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 
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## 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 
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## 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 
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## 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 
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## 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
## 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
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## 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
## 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 
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## 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
## 0, 0, 0, 0, 0, 0, 0), c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
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## 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
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## 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 
## 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 
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## 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
## 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
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## 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
## 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1), c(0, 0, 0, 0, 0, 0, 0, 
## 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
## 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
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## 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
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## 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
## 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
## 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
## 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
## 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 
## 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 
## 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
## 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
## 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
## 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
## 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 
## 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
## 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
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## 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
## 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
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## 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
## 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
## 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 
## 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
## 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 
## 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0), c(1, 
## 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1, 0, 1, 1, 
## 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 
## 0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 
## 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 1, 
## 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 
## 1, 0, 0, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 
## 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 
## 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 0, 1, 
## 0, 0, 1, 0, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 1, 
## 1, 1, 1, 1, 1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1, 1, 1, 0, 
## 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 
## 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 1, 0, 
## 1, 0, 0, 1, 0, 1, 0, 1, 1, 1, 0, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 
## 0, 0, 1, 1, 1, 1, 0, 1, 1, 0, 0, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 
## 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 0, 1, 1, 
## 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 
## 1, 1, 1, 1, 1, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 
## 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 0, 0, 1, 
## 1, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 0, 0, 0, 
## 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 
## 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 
## 1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 
## 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 0, 1, 0, 1, 1, 1, 1, 1, 0, 
## 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 0, 1, 1, 
## 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 
## 1, 1, 1, 1, 0, 0, 1, 0, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1, 
## 1, 0, 0, 1, 1, 0, 0, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 
## 0, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1, 
## 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 
## 0, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 0, 
## 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 
## 1, 1, 1, 1, 1, 0, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 
## 1, 1, 1, 0), c(2, 2, 2, 1, 2, 2, 2, 2, 1, 2, 2, 2, 2, 1, 2, 2, 
## 2, 2, 2, 2, 2, 2, 1, 2, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1, 2, 1, 
## 2, 2, 2, 2, 2, 1, 2, 1, 2, 1, 2, 2, 2, 2, 2, 1, 2, 1, 2, 1, 2, 
## 1, 2, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1, 2, 2, 1, 1, 1, 2, 2, 
## 2, 2, 2, 2, 1, 2, 1, 2, 2, 2, 2, 2, 2, 2, 2, 1, 2, 2, 2, 1, 2, 
## 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1, 1, 1, 1, 2, 1, 1, 2, 1, 2, 
## 1, 2, 1, 1, 2, 2, 1, 1, 1, 2, 2, 2, 1, 2, 1, 1, 2, 1, 2, 2, 2, 
## 2, 1, 2, 1, 2, 2, 2, 2, 1, 2, 1, 1, 2, 2, 2, 2, 2, 2, 2, 1, 1, 
## 2, 1, 2, 2, 1, 2, 1, 1, 2, 2, 2, 2, 1, 1, 2, 1, 1, 2, 1, 2, 2, 
## 2, 2, 2, 2, 1, 2, 2, 1, 2, 2, 2, 2, 1, 2, 2, 1, 2, 2, 1, 2, 1, 
## 1, 1, 1, 1, 2, 1, 1, 2, 2, 1, 2, 2, 2, 2, 2, 2, 2, 2, 1, 2, 2, 
## 2, 2, 1, 1, 2, 1, 2, 1, 2, 2, 2, 2, 2, 2, 2, 2, 1, 2, 2, 2, 2, 
## 2, 2, 2, 2, 1, 2, 2, 1, 1, 1, 1, 2, 1, 2, 2, 2, 2, 1, 2, 1, 1, 
## 2, 1, 1, 1, 2, 2, 1, 1, 2, 2, 1, 1, 1, 2, 1, 2, 1, 2, 2, 1, 1, 
## 2, 1, 2, 1, 2, 2, 2, 2, 2, 2, 2, 2, 1, 2, 2, 1, 1, 1, 2, 1, 2, 
## 2, 1, 2, 2, 2, 2, 2, 2, 2, 2, 1, 1, 1, 2, 1, 2, 2, 2, 2, 2, 2, 
## 2, 2, 1, 2, 1, 2, 2, 2, 1, 2, 2, 2, 1, 1, 2, 2, 2, 2, 2, 2, 2, 
## 2, 1, 2, 2, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1, 1, 1, 2, 
## 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1, 2, 2, 2, 2, 1, 2, 1, 1, 1, 
## 2, 2, 1, 2, 2, 2, 2, 2, 2, 2, 2, 1, 2, 2, 2, 1, 2, 2, 2, 2, 1, 
## 1, 1, 2, 2, 2, 2, 1, 2, 1, 2, 1, 2, 1, 2, 2, 2, 2, 2, 2, 2, 2, 
## 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1, 2, 1, 1, 2, 2, 1, 2, 2, 2, 2, 
## 2, 1, 2, 1, 2, 2, 1, 2, 2, 2, 1, 1, 2, 2, 2, 2, 2, 2, 1, 2, 2, 
## 2, 1, 2, 1, 2, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1, 2, 
## 2, 1, 2, 2, 2, 2, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1, 2, 2, 2, 
## 2, 2, 1, 2, 2, 2, 2, 2, 2, 2, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 
## 1, 1, 2, 2, 2, 2, 1, 1, 2, 1, 2, 2, 2, 2, 1, 2, 1, 2, 1, 1, 2, 
## 2, 2, 2, 2, 1, 2, 2, 2, 2, 2, 2, 1, 2, 2, 2, 2, 2, 1, 1, 2, 2, 
## 2, 2, 2, 2, 2, 2, 1, 1, 2, 2, 1, 2, 2, 2, 1, 1, 2, 2, 2, 1, 2, 
## 2, 2, 1, 2, 2, 2, 2, 2, 1, 1, 2, 2, 1, 2, 1, 1, 2, 2, 1, 2, 1, 
## 2, 1, 1, 2, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 1, 1, 2, 2, 2, 2, 2, 
## 2, 1, 2, 2, 2, 2, 2, 2, 2, 2, 1, 1, 2, 2, 2, 2, 2, 1, 2, 2, 2, 
## 2, 2, 2, 2, 2, 2, 2, 2, 2, 1)), control = list(20, 7, 0, 4, 5, 
##     2, 0, 30, 0))
##   n= 677 
## 
##          CP nsplit rel error
## 1 0.6701031      0 1.0000000
## 2 0.2164948      1 0.3298969
## 
## Variable importance
## depression_severityModerate             phq_score(9,14] 
##                          49                          49 
##               bmi(39.9,100]    who_bmiClass III Obesity 
##                           1                           1 
## 
## Node number 1: 677 observations,    complexity param=0.6701031
##   predicted class=0  expected loss=0.2865583  P(node) =1
##     class counts:   194   483
##    probabilities: 0.287 0.713 
##   left son=2 (130 obs) right son=3 (547 obs)
##   Primary splits:
##       phq_score(9,14]             < 0.5 to the right, improve=163.79160, (0 missing)
##       depression_severityModerate < 0.5 to the right, improve=163.79160, (0 missing)
##       suicidal0                   < 0.5 to the left,  improve= 69.47878, (0 missing)
##       phq_score(4,9]              < 0.5 to the left,  improve= 67.47916, (0 missing)
##       anxiousness0                < 0.5 to the left,  improve= 60.48914, (0 missing)
##   Surrogate splits:
##       depression_severityModerate < 0.5 to the right, agree=1.000, adj=1.000, (0 split)
##       bmi(39.9,100]               < 0.5 to the right, agree=0.811, adj=0.015, (0 split)
##       who_bmiClass III Obesity    < 0.5 to the right, agree=0.811, adj=0.015, (0 split)
## 
## Node number 2: 130 observations
##   predicted class=1  expected loss=0  P(node) =0.1920236
##     class counts:   130     0
##    probabilities: 1.000 0.000 
## 
## Node number 3: 547 observations
##   predicted class=0  expected loss=0.1170018  P(node) =0.8079764
##     class counts:    64   483
##    probabilities: 0.117 0.883

#accuracy of the tree 
accuracy <- tree$results$Accuracy
print(paste("Accuracy:", accuracy))

## [1] "Accuracy: 0.949398066993609" "Accuracy: 0.949398066993609"
## [3] "Accuracy: 0.826795397343992"

# get the average accuracy across all fold
accuracy <- mean(tree$results$Accuracy)
print(paste("Average Accuracy:", accuracy))

## [1] "Average Accuracy: 0.908530510443737"

tree1 <- valuation_df <- data.frame(tree = "tree2", accuracy = accuracy, Atrribute="gini",
                                       cv_K=5)
evaluation_df <- rbind(evaluation_df, tree1)
# visulize the tree
fancyRpartPlot(trained_model, caption = NULL)

Gain Ratio

folds <- createFolds(dataset$depressiveness, k = 5)  # create the folds
# control parameters 
train_control <- trainControl(method = "cv", index = folds)

# Compute the gain ratio of attributes for predicting the depressiveness
weights <- gain.ratio(depressiveness ~ ., data = dataset)

# Print the weights
print(weights)

##                      attr_importance
## id                       0.000000000
## school_year              0.000000000
## age                      0.000000000
## gender                   0.004677615
## bmi                      0.011351363
## who_bmi                  0.011051916
## phq_score                0.404717233
## depression_severity      0.404717233
## suicidal                 0.413593744
## depression_diagnosis     0.070324952
## depression_treatment     0.046224308
## gad_score                0.108969343
## anxiety_severity         0.108969343
## anxiousness              0.178526729
## anxiety_diagnosis        0.056732232
## anxiety_treatment        0.040011292
## epworth_score            0.054659804
## sleepiness               0.040786810

#build the model 
tree <- train(depressiveness ~ ., data = dataset, trControl = train_control, 
               method = "rpart", parms = list(split = weights))

trained_model <- tree$finalModel
trained_model

## n= 677 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 677 194 0 (0.28655835 0.71344165)  
##   2) phq_score(9,14]>=0.5 130   0 1 (1.00000000 0.00000000) *
##   3) phq_score(9,14]< 0.5 547  64 0 (0.11700183 0.88299817)  
##     6) suicidal0< 0.5 42   0 1 (1.00000000 0.00000000) *
##     7) suicidal0>=0.5 505  22 0 (0.04356436 0.95643564) *

trained_model$variable.importance

## depression_severityModerate             phq_score(9,14] 
##                  163.791596                  163.791596 
##                   suicidal0   depression_severitySevere 
##                   70.940598                   10.134371 
##            phq_score(19,27]        epworth_score(17,24] 
##                   10.134371                    6.756247 
##               bmi(39.9,100]    who_bmiClass III Obesity 
##                    2.519871                    2.519871 
##              bmi(34.9,39.9]     who_bmiClass II Obesity 
##                    1.689062                    1.689062

#accuracy of the tree 
accuracy <- tree$results$Accuracy
print(paste("Accuracy:", accuracy))

## [1] "Accuracy: 0.963440616769501" "Accuracy: 0.938671670373938"
## [3] "Accuracy: 0.826565827539792"

# get the average accuracy across all fold
accuracy <- mean(tree$results$Accuracy)
print(paste("Average Accuracy:", accuracy))

## [1] "Average Accuracy: 0.909559371561077"

tree1 <- valuation_df <- data.frame(tree = "tree3", accuracy = accuracy, Atrribute="Gain ratio",
                                       cv_K=5)
evaluation_df <- rbind(evaluation_df, tree1)
# visulize the tree
fancyRpartPlot(trained_model, caption = NULL)

evaluation_df

##    tree  accuracy        Atrribute cv_K
## 1 tree1 0.8942690 information gain    5
## 2 tree2 0.9085305             gini    5
## 3 tree3 0.9095594       Gain ratio    5

DT with k-validation 7 and attribute selection information gain, gain ratio , gini index

folds <- createFolds(dataset$depressiveness, k = 7)  # create the folds
# control parameters 
train_control <- trainControl(method = "cv", index = folds)

train_control

## $method
## [1] "cv"
## 
## $number
## [1] 10
## 
## $repeats
## [1] NA
## 
## $search
## [1] "grid"
## 
## $p
## [1] 0.75
## 
## $initialWindow
## NULL
## 
## $horizon
## [1] 1
## 
## $fixedWindow
## [1] TRUE
## 
## $skip
## [1] 0
## 
## $verboseIter
## [1] FALSE
## 
## $returnData
## [1] TRUE
## 
## $returnResamp
## [1] "final"
## 
## $savePredictions
## [1] FALSE
## 
## $classProbs
## [1] FALSE
## 
## $summaryFunction
## function (data, lev = NULL, model = NULL) 
## {
##     if (is.character(data$obs)) 
##         data$obs <- factor(data$obs, levels = lev)
##     postResample(data[, "pred"], data[, "obs"])
## }
## <bytecode: 0x7fccf1684668>
## <environment: namespace:caret>
## 
## $selectionFunction
## [1] "best"
## 
## $preProcOptions
## $preProcOptions$thresh
## [1] 0.95
## 
## $preProcOptions$ICAcomp
## [1] 3
## 
## $preProcOptions$k
## [1] 5
## 
## $preProcOptions$freqCut
## [1] 19
## 
## $preProcOptions$uniqueCut
## [1] 10
## 
## $preProcOptions$cutoff
## [1] 0.9
## 
## 
## $sampling
## NULL
## 
## $index
## $index$Fold1
##  [1]   6  14  21  22  23  28  35  47  55  68  73  83  88  91  92  97  99 107 124
## [20] 125 126 127 160 170 184 186 202 214 220 230 233 248 250 251 269 273 281 284
## [39] 286 290 292 296 301 310 319 321 346 365 382 383 386 390 393 396 402 414 418
## [58] 425 430 449 454 459 479 480 488 490 497 501 506 524 537 541 542 543 565 570
## [77] 576 584 589 590 591 592 593 601 606 610 615 617 619 624 640 643 658 667 671
## [96] 674
## 
## $index$Fold2
##  [1]   1   9  10  18  20  32  41  45  52  54  58  62  64  66  69  76  78 105 108
## [20] 109 110 112 128 136 145 146 147 150 154 165 166 175 190 198 209 216 240 242
## [39] 245 249 272 294 307 308 309 328 329 342 343 350 355 363 364 385 389 391 403
## [58] 412 415 423 424 426 439 443 444 446 450 455 457 472 475 486 487 525 531 545
## [77] 550 551 553 555 561 566 567 572 580 595 599 602 608 609 611 614 630 635 638
## [96] 657 666
## 
## $index$Fold3
##  [1]   5  13  27  46  56  63  72  81  95  98 103 114 130 132 138 161 162 164 167
## [20] 169 173 176 187 189 195 196 210 213 217 221 229 231 234 241 243 255 259 260
## [39] 261 262 282 283 285 288 289 313 315 327 339 353 354 356 359 370 374 384 387
## [58] 388 409 427 428 438 453 460 461 469 471 494 499 500 508 509 522 527 529 536
## [77] 539 544 552 554 563 569 583 598 605 607 618 621 622 623 628 637 641 644 655
## [96] 664 668
## 
## $index$Fold4
##  [1]  17  19  24  25  30  33  34  43  51  59  71  74  79  84  87  89  94 106 117
## [20] 121 134 141 143 149 151 158 171 177 179 181 182 188 199 200 205 212 222 223
## [39] 239 246 253 258 263 277 278 287 300 306 314 318 320 323 324 347 351 357 367
## [58] 371 372 377 378 380 404 405 408 417 420 421 422 429 434 436 440 452 476 477
## [77] 482 492 496 512 514 528 535 548 586 603 620 626 629 645 647 654 656 660 661
## [96] 662 677
## 
## $index$Fold5
##  [1]   7  12  29  31  42  50  60  75  77  96 100 101 102 133 137 142 159 168 174
## [20] 180 185 197 203 211 215 219 225 227 235 238 266 267 270 274 275 279 293 295
## [39] 298 302 311 312 317 322 332 334 337 341 345 362 373 399 400 401 407 410 413
## [58] 419 435 458 463 468 478 481 483 493 495 502 510 513 515 516 518 526 530 532
## [77] 538 562 564 571 575 578 582 588 597 600 604 613 625 631 648 650 651 669 672
## [96] 673
## 
## $index$Fold6
##  [1]   3  11  15  39  40  44  53  61  65  82  85 115 118 120 122 131 144 152 153
## [20] 172 178 183 192 193 194 201 204 207 218 224 228 232 236 237 252 254 256 257
## [39] 268 280 291 297 299 304 316 325 326 331 338 348 349 361 366 368 369 375 376
## [58] 379 394 432 437 442 445 447 451 464 465 466 467 470 484 485 489 498 504 520
## [77] 523 540 546 547 558 560 577 585 587 594 612 616 632 633 634 652 653 663 670
## [96] 675 676
## 
## $index$Fold7
##  [1]   2   4   8  16  26  36  37  38  48  49  57  67  70  80  86  90  93 104 111
## [20] 113 116 119 123 129 135 139 140 148 155 156 157 163 191 206 208 226 244 247
## [39] 264 265 271 276 303 305 330 333 335 336 340 344 352 358 360 381 392 395 397
## [58] 398 406 411 416 431 433 441 448 456 462 473 474 491 503 505 507 511 517 519
## [77] 521 533 534 549 556 557 559 568 573 574 579 581 596 627 636 639 642 646 649
## [96] 659 665
## 
## 
## $indexOut
## NULL
## 
## $indexFinal
## NULL
## 
## $timingSamps
## [1] 0
## 
## $predictionBounds
## [1] FALSE FALSE
## 
## $seeds
## [1] NA
## 
## $adaptive
## $adaptive$min
## [1] 5
## 
## $adaptive$alpha
## [1] 0.05
## 
## $adaptive$method
## [1] "gls"
## 
## $adaptive$complete
## [1] TRUE
## 
## 
## $trim
## [1] FALSE
## 
## $allowParallel
## [1] TRUE

Information gain - k=7

#build the model
tree <- train(depressiveness ~ ., data = dataset, trControl = train_control, 
               method = "rpart", parms = list(split = "information"))

# train result
trained_model <- tree$finalModel
trained_model

## n= 677 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 677 194 0 (0.2865583 0.7134417)  
##   2) phq_score(9,14]>=0.5 130   0 1 (1.0000000 0.0000000) *
##   3) phq_score(9,14]< 0.5 547  64 0 (0.1170018 0.8829982) *

#features importance
trained_model$variable.importance

## depression_severityModerate             phq_score(9,14] 
##                  208.133991                  208.133991 
##               bmi(39.9,100]    who_bmiClass III Obesity 
##                    3.202061                    3.202061

#accuracy of the tree 
accuracy <- tree$results$Accuracy
print(paste("Accuracy:", accuracy))

## [1] "Accuracy: 0.936716888666559" "Accuracy: 0.936716888666559"
## [3] "Accuracy: 0.793983958352764"

# get the average accuracy across all fold
accuracy <- mean(tree$results$Accuracy)
print(paste("Average Accuracy:", accuracy))

## [1] "Average Accuracy: 0.889139245228627"

res <- valuation_df <- data.frame(tree = "tree4", accuracy = accuracy, Atrribute="information gain",
                                       cv_K=7)
evaluation_df <- rbind(evaluation_df, res)

Gini index - k=7

folds <- createFolds(dataset$depressiveness, k = 7)  # create the folds
# control parameters 
train_control <- trainControl(method = "cv", index = folds)

#build the model
tree <- train(depressiveness ~ ., data = dataset, trControl = train_control, 
               method = "rpart", parms = list(split = "gini"))

# train result
trained_model <- tree$finalModel
trained_model

## n= 677 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 677 194 0 (0.28655835 0.71344165)  
##   2) phq_score(9,14]>=0.5 130   0 1 (1.00000000 0.00000000) *
##   3) phq_score(9,14]< 0.5 547  64 0 (0.11700183 0.88299817)  
##     6) suicidal0< 0.5 42   0 1 (1.00000000 0.00000000) *
##     7) suicidal0>=0.5 505  22 0 (0.04356436 0.95643564) *

#features importance
trained_model$variable.importance

## depression_severityModerate             phq_score(9,14] 
##                  163.791596                  163.791596 
##                   suicidal0   depression_severitySevere 
##                   70.940598                   10.134371 
##            phq_score(19,27]        epworth_score(17,24] 
##                   10.134371                    6.756247 
##               bmi(39.9,100]    who_bmiClass III Obesity 
##                    2.519871                    2.519871 
##              bmi(34.9,39.9]     who_bmiClass II Obesity 
##                    1.689062                    1.689062

#accuracy of the tree 
accuracy <- tree$results$Accuracy
print(paste("Accuracy:", accuracy))

## [1] "Accuracy: 0.94018042613805"  "Accuracy: 0.9369784556947"  
## [3] "Accuracy: 0.766397751456212"

# get the average accuracy across all fold
accuracy <- mean(tree$results$Accuracy)
print(paste("Average Accuracy:", accuracy))

## [1] "Average Accuracy: 0.881185544429654"

res <- valuation_df <- data.frame(tree = "tree5", accuracy = accuracy, Atrribute="Gini index",
                                       cv_K=7)
evaluation_df <- rbind(evaluation_df, res)

Gain Ratio - k=7

folds <- createFolds(dataset$depressiveness, k = 7)  # create the folds
# control parameters 
train_control <- trainControl(method = "cv", index = folds)

# Compute the gain ratio of attributes for predicting the depressiveness
weights <- gain.ratio(depressiveness ~ ., data = dataset)

# Print the weights
print(weights)

##                      attr_importance
## id                       0.000000000
## school_year              0.000000000
## age                      0.000000000
## gender                   0.004677615
## bmi                      0.011351363
## who_bmi                  0.011051916
## phq_score                0.404717233
## depression_severity      0.404717233
## suicidal                 0.413593744
## depression_diagnosis     0.070324952
## depression_treatment     0.046224308
## gad_score                0.108969343
## anxiety_severity         0.108969343
## anxiousness              0.178526729
## anxiety_diagnosis        0.056732232
## anxiety_treatment        0.040011292
## epworth_score            0.054659804
## sleepiness               0.040786810

#build the model 
tree <- train(depressiveness ~ ., data = dataset, trControl = train_control, 
               method = "rpart", parms = list(split = weights))

#build the model
tree <- train(depressiveness ~ ., data = dataset, trControl = train_control, 
               method = "rpart", parms = list(split = weights))

# train result
trained_model <- tree$finalModel
trained_model

## n= 677 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 677 194 0 (0.2865583 0.7134417)  
##   2) phq_score(9,14]>=0.5 130   0 1 (1.0000000 0.0000000) *
##   3) phq_score(9,14]< 0.5 547  64 0 (0.1170018 0.8829982) *

#features importance
trained_model$variable.importance

## depression_severityModerate             phq_score(9,14] 
##                  163.791596                  163.791596 
##               bmi(39.9,100]    who_bmiClass III Obesity 
##                    2.519871                    2.519871

#accuracy of the tree 
accuracy <- tree$results$Accuracy
print(paste("Accuracy:", accuracy))

## [1] "Accuracy: 0.945599569283467" "Accuracy: 0.945599569283467"
## [3] "Accuracy: 0.793198833334746"

# get the average accuracy across all fold
accuracy <- mean(tree$results$Accuracy)
print(paste("Average Accuracy:", accuracy))

## [1] "Average Accuracy: 0.894799323967227"

res <- valuation_df <- data.frame(tree = "tree6", accuracy = accuracy, Atrribute="Gain Ratio",
                                       cv_K=7)
evaluation_df <- rbind(evaluation_df, res)

fancyRpartPlot(trained_model, caption = NULL)

DT with k-validation 10 and attribute selection information gain, gain ratio , gini index

information gain - k=10

folds <- createFolds(dataset$depressiveness, k = 10)  # create the folds

# control parameters 
train_control <- trainControl(method = "cv", index = folds)

#build the model
tree <- train(depressiveness ~ ., data = dataset, trControl = train_control, 
               method = "rpart", parms = list(split = "information"))

# train result
trained_model <- tree$finalModel
trained_model

## n= 677 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 677 194 0 (0.2865583 0.7134417)  
##   2) phq_score(9,14]>=0.5 130   0 1 (1.0000000 0.0000000) *
##   3) phq_score(9,14]< 0.5 547  64 0 (0.1170018 0.8829982) *

#features importance
trained_model$variable.importance

## depression_severityModerate             phq_score(9,14] 
##                  208.133991                  208.133991 
##               bmi(39.9,100]    who_bmiClass III Obesity 
##                    3.202061                    3.202061

#accuracy of the tree 
accuracy <- tree$results$Accuracy
print(paste("Accuracy:", accuracy))

## [1] "Accuracy: 0.91334268755552"  "Accuracy: 0.91663216123973" 
## [3] "Accuracy: 0.827798171587816"

# get the average accuracy across all fold
accuracy <- mean(tree$results$Accuracy)
print(paste("Average Accuracy:", accuracy))

## [1] "Average Accuracy: 0.885924340127689"

res <- valuation_df <- data.frame(tree = "tree7", accuracy = accuracy, Atrribute="information gain",
                                       cv_K=10)
evaluation_df <- rbind(evaluation_df, res)

Gini index - k=10

folds <- createFolds(dataset$depressiveness, k = 10)  # create the folds

# control parameters 
train_control <- trainControl(method = "cv", index = folds)

#build the model
tree <- train(depressiveness ~ ., data = dataset, trControl = train_control, 
               method = "rpart", parms = list(split = "gini"))

# train result
trained_model <- tree$finalModel
trained_model

## n= 677 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 677 194 0 (0.28655835 0.71344165)  
##   2) phq_score(9,14]>=0.5 130   0 1 (1.00000000 0.00000000) *
##   3) phq_score(9,14]< 0.5 547  64 0 (0.11700183 0.88299817)  
##     6) suicidal0< 0.5 42   0 1 (1.00000000 0.00000000) *
##     7) suicidal0>=0.5 505  22 0 (0.04356436 0.95643564) *

#features importance
trained_model$variable.importance

## depression_severityModerate             phq_score(9,14] 
##                  163.791596                  163.791596 
##                   suicidal0   depression_severitySevere 
##                   70.940598                   10.134371 
##            phq_score(19,27]        epworth_score(17,24] 
##                   10.134371                    6.756247 
##               bmi(39.9,100]    who_bmiClass III Obesity 
##                    2.519871                    2.519871 
##              bmi(34.9,39.9]     who_bmiClass II Obesity 
##                    1.689062                    1.689062

#accuracy of the tree 
accuracy <- tree$results$Accuracy
print(paste("Accuracy:", accuracy))

## [1] "Accuracy: 0.893627284718297" "Accuracy: 0.880196236776225"
## [3] "Accuracy: 0.807499259737813"

# get the average accuracy across all fold
accuracy <- mean(tree$results$Accuracy)
print(paste("Average Accuracy:", accuracy))

## [1] "Average Accuracy: 0.860440927077445"

res <- valuation_df <- data.frame(tree = "tree8", accuracy = accuracy, Atrribute="Gini index",
                                       cv_K=10)
evaluation_df <- rbind(evaluation_df, res)

Gain ratio - k=10

folds <- createFolds(dataset$depressiveness, k = 10)  # create the folds

# control parameters 
train_control <- trainControl(method = "cv", index = folds)

# Compute the gain ratio of attributes for predicting the depressiveness
weights <- gain.ratio(depressiveness ~ ., data = dataset)

# Print the weights
print(weights)

##                      attr_importance
## id                       0.000000000
## school_year              0.000000000
## age                      0.000000000
## gender                   0.004677615
## bmi                      0.011351363
## who_bmi                  0.011051916
## phq_score                0.404717233
## depression_severity      0.404717233
## suicidal                 0.413593744
## depression_diagnosis     0.070324952
## depression_treatment     0.046224308
## gad_score                0.108969343
## anxiety_severity         0.108969343
## anxiousness              0.178526729
## anxiety_diagnosis        0.056732232
## anxiety_treatment        0.040011292
## epworth_score            0.054659804
## sleepiness               0.040786810

#build the model 
tree <- train(depressiveness ~ ., data = dataset, trControl = train_control, 
               method = "rpart", parms = list(split = weights))

# train result
trained_model <- tree$finalModel
trained_model

## n= 677 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 677 194 0 (0.28655835 0.71344165)  
##   2) phq_score(9,14]>=0.5 130   0 1 (1.00000000 0.00000000) *
##   3) phq_score(9,14]< 0.5 547  64 0 (0.11700183 0.88299817)  
##     6) suicidal0< 0.5 42   0 1 (1.00000000 0.00000000) *
##     7) suicidal0>=0.5 505  22 0 (0.04356436 0.95643564) *

#features importance
trained_model$variable.importance

## depression_severityModerate             phq_score(9,14] 
##                  163.791596                  163.791596 
##                   suicidal0   depression_severitySevere 
##                   70.940598                   10.134371 
##            phq_score(19,27]        epworth_score(17,24] 
##                   10.134371                    6.756247 
##               bmi(39.9,100]    who_bmiClass III Obesity 
##                    2.519871                    2.519871 
##              bmi(34.9,39.9]     who_bmiClass II Obesity 
##                    1.689062                    1.689062

#accuracy of the tree 
accuracy <- tree$results$Accuracy
print(paste("Accuracy:", accuracy))

## [1] "Accuracy: 0.907600960782397" "Accuracy: 0.905466313820164"
## [3] "Accuracy: 0.808289539209811"

# get the average accuracy across all fold
accuracy <- mean(tree$results$Accuracy)
print(paste("Average Accuracy:", accuracy))

## [1] "Average Accuracy: 0.873785604604124"

res <- valuation_df <- data.frame(tree = "tree9", accuracy = accuracy, Atrribute="Gain Ratio",
                                       cv_K=10)
evaluation_df <- rbind(evaluation_df, res)

evaluation_df

##    tree  accuracy        Atrribute cv_K
## 1 tree1 0.8942690 information gain    5
## 2 tree2 0.9085305             gini    5
## 3 tree3 0.9095594       Gain ratio    5
## 4 tree4 0.8891392 information gain    7
## 5 tree5 0.8811855       Gini index    7
## 6 tree6 0.8947993       Gain Ratio    7
## 7 tree7 0.8859243 information gain   10
## 8 tree8 0.8604409       Gini index   10
## 9 tree9 0.8737856       Gain Ratio   10

Clustering

# include the libraries and import the dataset 
library('superml')

## Loading required package: R6

library(cluster)
library(factoextra)

## Welcome! Want to learn more? See two factoextra-related books at https://goo.gl/ve3WBa

# import the dataset 
df<-dataset

head(df,10)

## # A tibble: 10 × 19
##       id school_year   age gender bmi      who_bmi phq_score depression_severity
##    <dbl>       <dbl> <dbl> <chr>  <fct>    <chr>   <fct>     <chr>              
##  1     1           1    19 male   (29.9,3… Class … (4,9]     Mild               
##  2     2           1    18 male   (18.4,2… Normal  (4,9]     Mild               
##  3     3           1    19 male   (24.9,2… Overwe… (4,9]     Mild               
##  4     4           1    18 female (18.4,2… Normal  (14,19]   Moderately severe  
##  5     5           1    18 male   (24.9,2… Overwe… (4,9]     Mild               
##  6     6           1    18 male   (18.4,2… Normal  (0,4]     None-minimal       
##  7     7           1    18 male   (18.4,2… Normal  (4,9]     Mild               
##  8     8           1    19 male   (18.4,2… Normal  (0,4]     None-minimal       
##  9     9           1    20 male   (18.4,2… Normal  (9,14]    Moderate           
## 10    10           1    19 male   (18.4,2… Normal  (4,9]     Mild               
## # ℹ 11 more variables: depressiveness <fct>, suicidal <fct>,
## #   depression_diagnosis <fct>, depression_treatment <fct>, gad_score <fct>,
## #   anxiety_severity <chr>, anxiousness <fct>, anxiety_diagnosis <fct>,
## #   anxiety_treatment <fct>, epworth_score <fct>, sleepiness <fct>

str(df)

## tibble [677 × 19] (S3: tbl_df/tbl/data.frame)
##  $ id                  : num [1:677] 1 2 3 4 5 6 7 8 9 10 ...
##  $ school_year         : num [1:677] 1 1 1 1 1 1 1 1 1 1 ...
##  $ age                 : num [1:677] 19 18 19 18 18 18 18 19 20 19 ...
##  $ gender              : chr [1:677] "male" "male" "male" "female" ...
##  $ bmi                 : Factor w/ 6 levels "(18,18.4]","(18.4,24.9]",..: 4 2 3 2 3 2 2 2 2 2 ...
##  $ who_bmi             : chr [1:677] "Class I Obesity" "Normal" "Overweight" "Normal" ...
##  $ phq_score           : Factor w/ 5 levels "(0,4]","(4,9]",..: 2 2 2 4 2 1 2 1 3 2 ...
##  $ depression_severity : chr [1:677] "Mild" "Mild" "Mild" "Moderately severe" ...
##  $ depressiveness      : Factor w/ 2 levels "1","0": 2 2 2 1 2 2 2 2 1 2 ...
##  $ suicidal            : Factor w/ 2 levels "1","0": 2 2 2 1 2 2 2 2 2 2 ...
##  $ depression_diagnosis: Factor w/ 2 levels "1","0": 2 2 2 2 2 2 2 2 2 2 ...
##  $ depression_treatment: Factor w/ 2 levels "1","0": 2 2 2 2 2 2 2 2 2 2 ...
##  $ gad_score           : Factor w/ 4 levels "(0,4]","(4,9]",..: 3 2 2 4 3 1 1 2 2 1 ...
##  $ anxiety_severity    : chr [1:677] "Moderate" "Mild" "Mild" "Severe" ...
##  $ anxiousness         : Factor w/ 2 levels "1","0": 1 2 2 1 1 2 2 2 2 2 ...
##  $ anxiety_diagnosis   : Factor w/ 2 levels "1","0": 2 2 2 2 2 2 2 2 2 2 ...
##  $ anxiety_treatment   : Factor w/ 2 levels "1","0": 2 2 2 2 2 2 2 2 2 2 ...
##  $ epworth_score       : Factor w/ 4 levels "(0,10]","(10,14]",..: 1 2 1 2 1 1 1 1 1 1 ...
##  $ sleepiness          : Factor w/ 2 levels "1","0": 2 1 2 1 2 2 2 2 2 2 ...
##  - attr(*, "na.action")= 'omit' Named int [1:77] 18 27 30 32 55 56 67 118 124 147 ...
##   ..- attr(*, "names")= chr [1:77] "18" "27" "30" "32" ...

let’s prepocess the data before applay clustering as well as kmeans deal only with numeric we need to convert factor and chr to numeric value, like labels encoding

# using label encoder
encoding <- LabelEncoder$new()

# Identify categorical columns
categorical_cols <- dataset %>%
  select_if(is.character) %>%
  names()

categorical_cols

## [1] "gender"              "who_bmi"             "depression_severity"
## [4] "anxiety_severity"

# Convert categorical columns to factors
df[categorical_cols] <- lapply(df[categorical_cols], as.factor)

str(df)

## tibble [677 × 19] (S3: tbl_df/tbl/data.frame)
##  $ id                  : num [1:677] 1 2 3 4 5 6 7 8 9 10 ...
##  $ school_year         : num [1:677] 1 1 1 1 1 1 1 1 1 1 ...
##  $ age                 : num [1:677] 19 18 19 18 18 18 18 19 20 19 ...
##  $ gender              : Factor w/ 2 levels "female","male": 2 2 2 1 2 2 2 2 2 2 ...
##  $ bmi                 : Factor w/ 6 levels "(18,18.4]","(18.4,24.9]",..: 4 2 3 2 3 2 2 2 2 2 ...
##  $ who_bmi             : Factor w/ 6 levels "Class I Obesity",..: 1 4 5 4 5 4 4 4 4 4 ...
##  $ phq_score           : Factor w/ 5 levels "(0,4]","(4,9]",..: 2 2 2 4 2 1 2 1 3 2 ...
##  $ depression_severity : Factor w/ 5 levels "Mild","Moderate",..: 1 1 1 3 1 4 1 4 2 1 ...
##  $ depressiveness      : Factor w/ 2 levels "1","0": 2 2 2 1 2 2 2 2 1 2 ...
##  $ suicidal            : Factor w/ 2 levels "1","0": 2 2 2 1 2 2 2 2 2 2 ...
##  $ depression_diagnosis: Factor w/ 2 levels "1","0": 2 2 2 2 2 2 2 2 2 2 ...
##  $ depression_treatment: Factor w/ 2 levels "1","0": 2 2 2 2 2 2 2 2 2 2 ...
##  $ gad_score           : Factor w/ 4 levels "(0,4]","(4,9]",..: 3 2 2 4 3 1 1 2 2 1 ...
##  $ anxiety_severity    : Factor w/ 4 levels "Mild","Moderate",..: 2 1 1 4 2 3 3 1 1 3 ...
##  $ anxiousness         : Factor w/ 2 levels "1","0": 1 2 2 1 1 2 2 2 2 2 ...
##  $ anxiety_diagnosis   : Factor w/ 2 levels "1","0": 2 2 2 2 2 2 2 2 2 2 ...
##  $ anxiety_treatment   : Factor w/ 2 levels "1","0": 2 2 2 2 2 2 2 2 2 2 ...
##  $ epworth_score       : Factor w/ 4 levels "(0,10]","(10,14]",..: 1 2 1 2 1 1 1 1 1 1 ...
##  $ sleepiness          : Factor w/ 2 levels "1","0": 2 1 2 1 2 2 2 2 2 2 ...
##  - attr(*, "na.action")= 'omit' Named int [1:77] 18 27 30 32 55 56 67 118 124 147 ...
##   ..- attr(*, "names")= chr [1:77] "18" "27" "30" "32" ...

categorical_cols <- dataset %>%
  select_if(is.factor) %>%
  names()
categorical_cols

##  [1] "bmi"                  "phq_score"            "depressiveness"      
##  [4] "suicidal"             "depression_diagnosis" "depression_treatment"
##  [7] "gad_score"            "anxiousness"          "anxiety_diagnosis"   
## [10] "anxiety_treatment"    "epworth_score"        "sleepiness"

for (col in categorical_cols)
{
    # Fit the LabelEncoder object to the data frame column
encoding$fit(df[[col]])

# Transform the data frame column using the LabelEncoder object
encoded_col <- encoding$fit_transform(df[[col]])
# Assign the encoded column to the data frame
df[[col]] <- encoded_col
}

head(df,10)

## # A tibble: 10 × 19
##       id school_year   age gender   bmi who_bmi    phq_score depression_severity
##    <dbl>       <dbl> <dbl> <fct>  <dbl> <fct>          <dbl> <fct>              
##  1     1           1    19 male       3 Class I O…         1 Mild               
##  2     2           1    18 male       1 Normal             1 Mild               
##  3     3           1    19 male       2 Overweight         1 Mild               
##  4     4           1    18 female     1 Normal             3 Moderately severe  
##  5     5           1    18 male       2 Overweight         1 Mild               
##  6     6           1    18 male       1 Normal             0 None-minimal       
##  7     7           1    18 male       1 Normal             1 Mild               
##  8     8           1    19 male       1 Normal             0 None-minimal       
##  9     9           1    20 male       1 Normal             2 Moderate           
## 10    10           1    19 male       1 Normal             1 Mild               
## # ℹ 11 more variables: depressiveness <dbl>, suicidal <dbl>,
## #   depression_diagnosis <dbl>, depression_treatment <dbl>, gad_score <dbl>,
## #   anxiety_severity <fct>, anxiousness <dbl>, anxiety_diagnosis <dbl>,
## #   anxiety_treatment <dbl>, epworth_score <dbl>, sleepiness <dbl>

# still some data has not changed lets convert them manually 
encoding$fit(df$gender)

# Transform the data frame column using the LabelEncoder object
encoded_col <- encoding$fit_transform(df$gender)

# Assign the encoded column to the data frame
df$gender <- encoded_col

# still some data has not changed lets convert them manually 
encoding$fit(df$depression_severity)

# Transform the data frame column using the LabelEncoder object
encoded_col <- encoding$fit_transform(df$depression_severity)

# Assign the encoded column to the data frame
df$depression_severity <- encoded_col


# still some data has not changed lets convert them manually 
encoding$fit(df$who_bmi)

# Transform the data frame column using the LabelEncoder object
encoded_col <- encoding$fit_transform(df$who_bmi)

# Assign the encoded column to the data frame
df$who_bmi <- encoded_col


# still some data has not changed lets convert them manually 
encoding$fit(df$anxiety_severity)

# Transform the data frame column using the LabelEncoder object
encoded_col <- encoding$fit_transform(df$anxiety_severity)

# Assign the encoded column to the data frame
df$anxiety_severity <- encoded_col

head(df)

## # A tibble: 6 × 19
##      id school_year   age gender   bmi who_bmi phq_score depression_severity
##   <dbl>       <dbl> <dbl>  <dbl> <dbl>   <dbl>     <dbl>               <dbl>
## 1     1           1    19      1     3       0         1                   0
## 2     2           1    18      1     1       3         1                   0
## 3     3           1    19      1     2       4         1                   0
## 4     4           1    18      0     1       3         3                   2
## 5     5           1    18      1     2       4         1                   0
## 6     6           1    18      1     1       3         0                   3
## # ℹ 11 more variables: depressiveness <dbl>, suicidal <dbl>,
## #   depression_diagnosis <dbl>, depression_treatment <dbl>, gad_score <dbl>,
## #   anxiety_severity <dbl>, anxiousness <dbl>, anxiety_diagnosis <dbl>,
## #   anxiety_treatment <dbl>, epworth_score <dbl>, sleepiness <dbl>

# lets spilt the data to target will not be used for clustering , also id not relvant to the case 
target <- data.frame(depressiveness = df$depressiveness)
predictors <- df[, -c(1,9)]
head(predictors)

## # A tibble: 6 × 17
##   school_year   age gender   bmi who_bmi phq_score depression_severity suicidal
##         <dbl> <dbl>  <dbl> <dbl>   <dbl>     <dbl>               <dbl>    <dbl>
## 1           1    19      1     3       0         1                   0        1
## 2           1    18      1     1       3         1                   0        1
## 3           1    19      1     2       4         1                   0        1
## 4           1    18      0     1       3         3                   2        0
## 5           1    18      1     2       4         1                   0        1
## 6           1    18      1     1       3         0                   3        1
## # ℹ 9 more variables: depression_diagnosis <dbl>, depression_treatment <dbl>,
## #   gad_score <dbl>, anxiety_severity <dbl>, anxiousness <dbl>,
## #   anxiety_diagnosis <dbl>, anxiety_treatment <dbl>, epworth_score <dbl>,
## #   sleepiness <dbl>

# Define a function to compute BCubed precision and recall
bcubed <- function(cluster, category) {
  # Check the input arguments
  if (length(cluster) != length(category)) {
    stop("cluster and category must have the same length")
  }
  if (any(is.na(cluster)) || any(is.na(category))) {
    stop("cluster and category must not contain NA values")
  }
  # Convert cluster and category to factors
  cluster <- factor(cluster)
  category <- factor(category)
  # Initialize the precision and recall vectors
  precision <- numeric(length(cluster))
  recall <- numeric(length(cluster))
  # Loop over each item
  for (i in 1:length(cluster)) {
    # Find the items in the same cluster as the current item
    same_cluster <- which(cluster == cluster[i])
    # Find the items in the same category as the current item
    same_category <- which(category == category[i])
    # Compute the precision and recall for the current item
    precision[i] <- length(intersect(same_cluster, same_category)) / length(same_cluster)
    recall[i] <- length(intersect(same_cluster, same_category)) / length(same_category)
  }
  # Return the average precision and recall
  return(c(precision = mean(precision), recall = mean(recall)))
}

“using elbow” — to determine the optimal number of clusters.

fviz_nbclust(predictors, kmeans, method = "wss")

Cluster with k=5

#perform k-means clustering with k = 5 clusters
km <- kmeans(predictors, centers = 5, nstart = 25)

#view results
km

## K-means clustering with 5 clusters of sizes 102, 74, 250, 88, 163
## 
## Cluster means:
##   school_year      age    gender      bmi  who_bmi  phq_score
## 1    1.450980 19.12745 0.5686275 1.196078 2.941176 0.03921569
## 2    1.851351 19.81081 0.2972973 1.472973 3.270270 2.51351351
## 3    1.564000 19.06800 0.4640000 1.372000 3.108000 1.29600000
## 4    3.454545 21.71591 0.6590909 1.386364 3.465909 0.00000000
## 5    3.380368 22.01840 0.4171779 1.411043 3.220859 1.31288344
##   depression_severity  suicidal depression_diagnosis depression_treatment
## 1           3.0098039 0.9901961            0.9509804            0.9607843
## 2           1.6351351 0.6621622            0.8378378            0.8378378
## 3           0.2960000 0.9120000            0.9360000            0.9400000
## 4           3.0000000 0.9886364            0.9431818            0.9545455
## 5           0.3128834 0.9202454            0.8773006            0.9141104
##   gad_score anxiety_severity anxiousness anxiety_diagnosis anxiety_treatment
## 1 0.2647059         1.500000  0.99019608         0.9705882         0.9607843
## 2 2.8108108         2.689189  0.01351351         0.8648649         0.8378378
## 3 0.9960000         0.812000  0.73200000         0.9360000         0.9160000
## 4 0.4204545         1.227273  0.97727273         0.9318182         0.9431818
## 5 1.0306748         0.607362  0.78527607         0.8957055         0.9325153
##   epworth_score sleepiness
## 1    0.05882353  0.9313725
## 2    0.63513514  0.5405405
## 3    0.16400000  0.8000000
## 4    0.02272727  0.9431818
## 5    0.17177914  0.7791411
## 
## Clustering vector:
##   [1] 3 3 3 2 3 1 3 1 3 3 1 3 3 3 3 3 3 3 1 3 1 3 2 1 2 1 1 3 3 1 1 1 3 2 3 3 3
##  [38] 1 3 1 1 3 3 3 3 1 3 3 3 3 3 1 3 1 3 5 3 3 2 1 1 3 3 1 3 1 3 1 3 1 1 3 3 3
##  [75] 3 3 2 1 3 3 1 1 3 3 3 2 3 3 3 3 3 3 1 3 3 1 1 3 3 1 2 3 3 3 1 3 3 2 3 3 1
## [112] 2 2 3 3 3 3 3 1 2 1 2 3 3 2 3 3 2 3 3 1 3 1 2 3 3 3 3 2 3 3 3 1 2 1 3 1 3
## [149] 3 3 3 3 2 3 1 1 1 3 3 1 3 2 2 1 3 5 3 2 3 3 2 3 2 1 5 3 3 1 2 3 3 3 1 1 1
## [186] 3 3 5 2 1 1 3 3 3 1 3 2 3 1 3 1 1 2 2 2 2 5 3 5 1 2 2 3 1 2 1 1 3 1 3 3 1
## [223] 3 2 1 1 1 3 3 3 1 3 3 2 3 1 3 4 5 3 3 3 2 3 4 3 5 3 5 3 1 3 3 3 5 2 3 2 3
## [260] 2 3 1 4 3 3 3 3 2 3 2 3 3 2 1 3 3 3 2 2 2 3 1 5 3 3 3 3 5 5 3 3 4 3 1 3 3
## [297] 3 3 1 1 3 2 3 3 5 3 3 3 5 5 3 5 1 2 3 5 2 3 3 3 3 2 3 1 3 3 3 4 1 3 3 5 1
## [334] 3 3 2 3 1 1 2 4 1 3 2 2 3 3 3 3 3 3 1 2 3 3 1 3 4 3 5 3 3 1 3 3 5 3 1 3 3
## [371] 2 3 3 5 3 3 3 3 1 3 3 3 3 1 3 3 5 1 1 3 3 3 3 3 3 3 3 1 3 5 3 5 3 4 3 2 3
## [408] 1 3 3 3 5 1 1 3 2 3 4 3 5 5 3 4 5 3 3 5 5 5 4 1 1 3 1 1 4 5 3 1 1 1 3 4 3
## [445] 4 4 5 3 5 3 4 3 2 5 4 4 5 3 2 5 5 1 3 2 4 5 3 2 3 4 3 5 2 5 5 2 4 3 5 2 3
## [482] 2 5 5 5 4 4 4 5 4 5 5 5 5 4 5 5 5 4 4 5 4 5 1 4 5 5 4 5 4 5 4 4 5 5 5 5 5
## [519] 4 5 4 4 5 4 5 4 4 5 5 5 5 5 4 5 5 5 4 5 4 4 4 5 2 5 4 4 4 5 5 4 5 5 5 5 5
## [556] 5 4 5 5 5 5 5 4 5 5 5 5 5 5 4 4 5 5 5 4 5 5 5 4 5 2 4 5 4 5 4 5 5 5 5 5 4
## [593] 5 5 5 5 5 5 2 4 4 5 2 5 5 4 5 4 4 5 5 5 5 5 4 4 5 4 5 5 5 4 5 5 5 5 2 5 5
## [630] 5 5 4 4 5 5 4 4 5 4 4 5 4 4 5 5 4 5 2 4 4 4 5 5 4 5 5 5 2 5 4 5 4 5 5 5 5
## [667] 5 5 4 4 5 4 5 5 4 4 2
## 
## Within cluster sum of squares by cluster:
## [1]  407.0294  663.9324 1258.0080  357.6023 1084.8221
##  (between_SS / total_SS =  50.0 %)
## 
## Available components:
## 
## [1] "cluster"      "centers"      "totss"        "withinss"     "tot.withinss"
## [6] "betweenss"    "size"         "iter"         "ifault"

#plot results of k-means 
fviz_cluster(km, data =predictors )

clusplot(df, km$cluster, color = TRUE, shade = TRUE, labels = 5)

# Evaluate and compare the results
silhouette_score <- silhouette(km$cluster, dist(predictors))
silhouette_score

##        cluster neighbor     sil_width
##   [1,]       3        1  0.1985604728
##   [2,]       3        1  0.3444898182
##   [3,]       3        1  0.3748770391
##   [4,]       2        3  0.2439215171
##   [5,]       3        1  0.3399324144
##   [6,]       1        3  0.5042503395
##   [7,]       3        1  0.2459758945
##   [8,]       1        3  0.3424330876
##   [9,]       3        1  0.2811681553
##  [10,]       3        1  0.2636563049
##  [11,]       1        3  0.5042503395
##  [12,]       3        1  0.2611952801
##  [13,]       3        1  0.3444898182
##  [14,]       3        2  0.2732914799
##  [15,]       3        1  0.2290234647
##  [16,]       3        1  0.3586856066
##  [17,]       3        1  0.3768467060
##  [18,]       3        1  0.2419436914
##  [19,]       1        3  0.5042503395
##  [20,]       3        2  0.2953145176
##  [21,]       1        3  0.5042503395
##  [22,]       3        1  0.3752158818
##  [23,]       2        3  0.1413920667
##  [24,]       1        3  0.5274816718
##  [25,]       2        3  0.0275355183
##  [26,]       1        3  0.5042503395
##  [27,]       1        3  0.5274816718
##  [28,]       3        1  0.2459758945
##  [29,]       3        1  0.2459758945
##  [30,]       1        3  0.5380517009
##  [31,]       1        3  0.5380517009
##  [32,]       1        3  0.2560003076
##  [33,]       3        1  0.3797705414
##  [34,]       2        3  0.0008967437
##  [35,]       3        1  0.2909772331
##  [36,]       3        1  0.3512394130
##  [37,]       3        1  0.2729167424
##  [38,]       1        3  0.5042503395
##  [39,]       3        1  0.2539782152
##  [40,]       1        3  0.4735298860
##  [41,]       1        3  0.5042503395
##  [42,]       3        2  0.3040905352
##  [43,]       3        1  0.3524996061
##  [44,]       3        1  0.3512394130
##  [45,]       3        2  0.2038803160
##  [46,]       1        3  0.2307334375
##  [47,]       3        2  0.2155079536
##  [48,]       3        1  0.2711047389
##  [49,]       3        2  0.3516831161
##  [50,]       3        1  0.2499180032
##  [51,]       3        1  0.4013781652
##  [52,]       1        3  0.2305104586
##  [53,]       3        2  0.2732914799
##  [54,]       1        3  0.2098164864
##  [55,]       3        2  0.0864671669
##  [56,]       5        4  0.1442341392
##  [57,]       3        1  0.2941468410
##  [58,]       3        1  0.2611952801
##  [59,]       2        3  0.2050130862
##  [60,]       1        3  0.5042503395
##  [61,]       1        3  0.0427784984
##  [62,]       3        1  0.1610987125
##  [63,]       3        2  0.3231576371
##  [64,]       1        4  0.4051345349
##  [65,]       3        5  0.3223627799
##  [66,]       1        3  0.3396748705
##  [67,]       3        1  0.3697634665
##  [68,]       1        3  0.3939388622
##  [69,]       3        5  0.1877609450
##  [70,]       1        4  0.2931543357
##  [71,]       1        3  0.4433286060
##  [72,]       3        2  0.2646893399
##  [73,]       3        1  0.2539782152
##  [74,]       3        1  0.2480756852
##  [75,]       3        2  0.2732914799
##  [76,]       3        2  0.2359900370
##  [77,]       2        3  0.1344159563
##  [78,]       1        3  0.5380517009
##  [79,]       3        1  0.3567825245
##  [80,]       3        2  0.2801391491
##  [81,]       1        3  0.2512486289
##  [82,]       1        3  0.5042503395
##  [83,]       3        1  0.2480756852
##  [84,]       3        1  0.2941823244
##  [85,]       3        1  0.3697634665
##  [86,]       2        3 -0.0058278610
##  [87,]       3        1  0.3697634665
##  [88,]       3        1  0.3500234273
##  [89,]       3        1  0.3399324144
##  [90,]       3        1  0.3768467060
##  [91,]       3        1  0.3655206646
##  [92,]       3        1  0.2611952801
##  [93,]       1        3  0.5042503395
##  [94,]       3        1  0.2711047389
##  [95,]       3        1  0.2682299167
##  [96,]       1        3  0.5042503395
##  [97,]       1        3  0.5042503395
##  [98,]       3        1  0.1960085716
##  [99,]       3        1  0.2631486519
## [100,]       1        3  0.5042503395
## [101,]       2        3  0.1344159563
## [102,]       3        2  0.2680698314
## [103,]       3        1  0.2539782152
## [104,]       3        1  0.3527279837
## [105,]       1        3  0.5042503395
## [106,]       3        1  0.3512394130
## [107,]       3        1  0.3697634665
## [108,]       2        3  0.0093266457
## [109,]       3        1  0.3636593172
## [110,]       3        1  0.3636593172
## [111,]       1        3  0.5274816718
## [112,]       2        3  0.1882003002
## [113,]       2        3  0.1952269480
## [114,]       3        1  0.1282455920
## [115,]       3        1  0.1409445724
## [116,]       3        1  0.3527279837
## [117,]       3        1  0.1082063037
## [118,]       3        1  0.1107452175
## [119,]       1        3  0.5042503395
## [120,]       2        3  0.1819571768
## [121,]       1        3  0.5274816718
## [122,]       2        3  0.1344159563
## [123,]       3        1  0.2611952801
## [124,]       3        2  0.2066315145
## [125,]       2        3  0.1577437047
## [126,]       3        1  0.3527279837
## [127,]       3        1  0.3586856066
## [128,]       2        5  0.0938932795
## [129,]       3        2  0.2063348470
## [130,]       3        2  0.2680698314
## [131,]       1        3  0.3424330876
## [132,]       3        1  0.2539782152
## [133,]       1        4  0.4066316773
## [134,]       2        1  0.2050683212
## [135,]       3        1  0.2636563049
## [136,]       3        1  0.2621965225
## [137,]       3        2  0.2680698314
## [138,]       3        1  0.2539782152
## [139,]       2        3  0.2487151148
## [140,]       3        1  0.1188974491
## [141,]       3        1  0.2480756852
## [142,]       3        1  0.2283500308
## [143,]       1        4  0.2931543357
## [144,]       2        3 -0.0788622652
## [145,]       1        3  0.3396748705
## [146,]       3        1  0.2331320937
## [147,]       1        3  0.3396748705
## [148,]       3        1  0.3697634665
## [149,]       3        1  0.2025336673
## [150,]       3        1  0.2539782152
## [151,]       3        2  0.2137593889
## [152,]       3        1  0.2459758945
## [153,]       2        3  0.1074130055
## [154,]       3        2  0.1673818505
## [155,]       1        3  0.4735298860
## [156,]       1        4  0.3029655435
## [157,]       1        3  0.4056187447
## [158,]       3        1  0.3768467060
## [159,]       3        2  0.3340313438
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## [604,]       5        3  0.1001487624
## [605,]       5        4  0.3095627651
## [606,]       4        1  0.3486062266
## [607,]       5        4  0.2424109517
## [608,]       4        1  0.3720550548
## [609,]       4        5  0.3844615055
## [610,]       5        4  0.1560148907
## [611,]       5        3  0.1673400205
## [612,]       5        3  0.2884144286
## [613,]       5        4  0.1854705612
## [614,]       5        4  0.1915755076
## [615,]       4        1  0.3986255906
## [616,]       4        1  0.3756124994
## [617,]       5        3  0.2720273845
## [618,]       4        5  0.3855320928
## [619,]       5        4  0.2063944157
## [620,]       5        4  0.2587315303
## [621,]       5        4  0.3336078302
## [622,]       4        1  0.3756124994
## [623,]       5        4  0.0694934937
## [624,]       5        3  0.3166827608
## [625,]       5        4  0.2343105370
## [626,]       5        4  0.3350884058
## [627,]       2        5  0.1006221848
## [628,]       5        4  0.1143396536
## [629,]       5        3  0.2986999041
## [630,]       5        4  0.1839706819
## [631,]       5        4  0.0842531815
## [632,]       4        5  0.3708447589
## [633,]       4        5  0.3708447589
## [634,]       5        4  0.2117478091
## [635,]       5        4  0.3509919657
## [636,]       4        5  0.3708447589
## [637,]       4        5  0.1839317179
## [638,]       5        4  0.1240974834
## [639,]       4        5  0.2503689724
## [640,]       4        1  0.3598083052
## [641,]       5        2  0.0771914851
## [642,]       4        1  0.4693352560
## [643,]       4        5  0.4789958293
## [644,]       5        4  0.0815549014
## [645,]       5        4  0.3204066485
## [646,]       4        1  0.4523993604
## [647,]       5        4  0.3747922491
## [648,]       2        5  0.1162089988
## [649,]       4        5  0.3296149648
## [650,]       4        5  0.2548424881
## [651,]       4        5  0.4032277250
## [652,]       5        2  0.1191359531
## [653,]       5        4  0.2947228888
## [654,]       4        5  0.4789958293
## [655,]       5        4  0.3747922491
## [656,]       5        4  0.2780794995
## [657,]       5        4  0.2448770292
## [658,]       2        4  0.1399307881
## [659,]       5        4  0.1867084266
## [660,]       4        5  0.3310540479
## [661,]       5        4  0.2172025107
## [662,]       4        5  0.3185714440
## [663,]       5        4  0.3095627651
## [664,]       5        4  0.2173834312
## [665,]       5        4  0.1769943204
## [666,]       5        4  0.1560148907
## [667,]       5        4  0.3747922491
## [668,]       5        4  0.3204066485
## [669,]       4        5  0.2845050939
## [670,]       4        5  0.4032277250
## [671,]       5        4  0.3163198638
## [672,]       4        5  0.4157629587
## [673,]       5        4  0.1769943204
## [674,]       5        4  0.1762610655
## [675,]       4        5  0.3855320928
## [676,]       4        5  0.3862034302
## [677,]       2        5  0.1148530907
## attr(,"Ordered")
## [1] FALSE
## attr(,"call")
## silhouette.default(x = km$cluster, dist = dist(predictors))
## attr(,"class")
## [1] "silhouette"

#decrase the cluster by 1 cause on label encoding we start with 0
nc<-km$cluster-1

# bcubed precision and recall 
bcubed(nc, df$depressiveness)

## precision    recall 
## 0.7076679 0.2806891

performing operations to analyze the differences between the Actual and cluster variables.

df1<-data.frame(Actual=df$depressiveness,cluster=nc)
head(df1,10)

##    Actual cluster
## 1       1       2
## 2       1       2
## 3       1       2
## 4       0       1
## 5       1       2
## 6       1       0
## 7       1       2
## 8       1       0
## 9       0       2
## 10      1       2

#length 
length(df1$Actual)

## [1] 677

length(df1$cluster)

## [1] 677

#count number of cluster with matched vlaues 
cont_table <- table(df1$Actual, df1$cluster)
cont_table

##    
##       0   1   2   3   4
##   0   1  64  77   1  51
##   1 101  10 173  87 112

differences <- sum(apply(cont_table, 1, max)) - sum(diag(cont_table))
print(paste("difference: ",differences))

## [1] "difference:  239"

Cluster with k=4

#perform k-means clustering with k = 4 clusters
km <- kmeans(predictors, centers = 4, nstart = 25)

#view results
km

## K-means clustering with 4 clusters of sizes 188, 163, 251, 75
## 
## Cluster means:
##   school_year      age    gender      bmi  who_bmi phq_score
## 1    3.446809 22.13830 0.4361702 1.398936 3.244681  1.132979
## 2    2.159509 19.92638 0.6196319 1.269939 3.171779  0.000000
## 3    1.565737 19.07570 0.4621514 1.370518 3.107570  1.294821
## 4    1.840000 19.81333 0.3066667 1.493333 3.226667  2.533333
##   depression_severity  suicidal depression_diagnosis depression_treatment
## 1           0.6861702 0.9308511            0.8776596            0.9148936
## 2           3.0000000 0.9938650            0.9570552            0.9631902
## 3           0.2948207 0.9123506            0.9362550            0.9402390
## 4           1.6666667 0.6533333            0.8400000            0.8400000
##   gad_score anxiety_severity anxiousness anxiety_diagnosis anxiety_treatment
## 1 0.9840426        0.6117021  0.81382979         0.8989362         0.9308511
## 2 0.2699387        1.4969325  0.98773006         0.9570552         0.9570552
## 3 1.0000000        0.8127490  0.72908367         0.9362550         0.9163347
## 4 2.7866667        2.6533333  0.02666667         0.8666667         0.8400000
##   epworth_score sleepiness
## 1    0.15425532  0.8031915
## 2    0.04294479  0.9325153
## 3    0.16334661  0.8007968
## 4    0.62666667  0.5466667
## 
## Clustering vector:
##   [1] 3 3 3 4 3 2 3 2 3 3 2 3 3 3 3 3 3 3 2 3 2 3 4 2 4 2 2 3 3 2 2 2 3 4 3 3 3
##  [38] 2 3 2 2 3 3 3 3 2 3 3 3 3 3 2 3 2 3 1 3 3 4 2 4 3 3 2 3 2 3 2 3 2 2 3 3 3
##  [75] 3 3 4 2 3 3 2 2 3 3 3 4 3 3 3 3 3 3 2 3 3 2 2 3 3 2 4 3 3 3 2 3 3 4 3 3 2
## [112] 4 4 3 3 3 3 3 2 4 2 4 3 3 4 3 3 4 3 3 2 3 2 4 3 3 3 3 4 3 3 3 2 4 2 3 2 3
## [149] 3 3 3 3 4 3 2 2 2 3 3 2 3 4 4 2 3 1 3 4 3 3 4 3 4 2 1 3 3 2 4 3 3 3 2 2 2
## [186] 3 3 1 4 2 2 3 3 3 2 3 4 3 2 3 2 2 4 4 4 4 1 3 1 2 4 4 3 2 4 2 2 3 2 3 3 2
## [223] 3 4 2 2 2 3 3 3 2 3 3 4 3 2 3 2 1 3 3 3 4 3 2 3 1 3 1 3 2 3 3 3 1 4 3 4 3
## [260] 4 3 2 1 3 3 3 3 4 3 4 3 3 4 2 3 3 3 4 4 4 3 2 1 3 3 3 3 1 1 3 3 2 3 2 3 3
## [297] 3 3 2 2 3 4 3 3 1 3 3 3 1 1 3 1 2 4 3 1 4 3 3 3 3 4 3 2 3 3 3 2 2 3 3 1 2
## [334] 3 3 4 3 2 2 4 2 2 3 4 4 3 3 3 3 3 3 2 4 3 3 2 3 2 3 1 3 3 2 3 3 1 3 2 3 3
## [371] 4 3 3 3 3 3 3 3 2 3 3 3 3 2 3 3 1 2 2 3 3 3 3 3 3 3 3 2 3 1 3 1 3 2 3 4 3
## [408] 2 3 3 3 1 2 2 3 4 3 2 3 1 1 3 2 1 3 3 1 1 1 2 2 2 3 2 2 2 1 3 2 2 2 3 2 3
## [445] 2 2 1 3 1 3 2 3 4 1 1 2 1 3 4 1 1 2 3 4 2 1 3 4 3 2 3 1 4 1 1 4 1 3 1 4 3
## [482] 4 1 1 1 2 2 2 1 2 1 1 1 1 2 1 1 1 2 2 1 2 1 2 2 1 1 1 1 2 1 2 2 1 1 1 1 1
## [519] 2 1 2 2 1 2 1 2 1 1 1 1 1 1 2 1 1 1 2 1 2 2 2 1 4 1 2 2 2 1 1 1 1 1 1 1 1
## [556] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 1 1 1 2 1 1 1 2 1 4 1 1 2 1 2 1 1 1 1 1 2
## [593] 1 1 1 1 1 1 4 2 2 1 4 1 1 2 1 2 2 1 1 1 1 1 2 2 1 1 1 1 1 2 1 1 1 1 4 1 1
## [630] 1 1 1 1 1 1 1 1 1 1 2 1 2 2 1 1 2 1 4 1 1 1 1 1 2 1 1 1 4 1 1 1 1 1 1 1 1
## [667] 1 1 1 1 1 1 1 1 1 1 4
## 
## Within cluster sum of squares by cluster:
## [1] 1403.5319  881.3742 1264.1195  698.1867
##  (between_SS / total_SS =  43.7 %)
## 
## Available components:
## 
## [1] "cluster"      "centers"      "totss"        "withinss"     "tot.withinss"
## [6] "betweenss"    "size"         "iter"         "ifault"

#plot results of k-means 
fviz_cluster(km, data =predictors )

clusplot(df, km$cluster, color = TRUE, shade = TRUE, labels = 4)

# Evaluate and compare the results
silhouette_score <- silhouette(km$cluster, dist(predictors))
silhouette_score

##        cluster neighbor     sil_width
##   [1,]       3        4  0.2210384908
##   [2,]       3        2  0.4087595983
##   [3,]       3        2  0.4146277604
##   [4,]       4        3  0.2369589392
##   [5,]       3        4  0.3488714356
##   [6,]       2        3  0.3051132267
##   [7,]       3        2  0.3370931029
##   [8,]       2        3  0.2082425793
##   [9,]       3        2  0.3197459319
##  [10,]       3        2  0.3341522301
##  [11,]       2        3  0.3051132267
##  [12,]       3        2  0.3160150888
##  [13,]       3        2  0.4087595983
##  [14,]       3        4  0.2766583685
##  [15,]       3        2  0.3148643680
##  [16,]       3        4  0.3764058542
##  [17,]       3        2  0.4432199819
##  [18,]       3        2  0.3181782403
##  [19,]       2        3  0.3051132267
##  [20,]       3        4  0.3002971074
##  [21,]       2        3  0.3051132267
##  [22,]       3        4  0.3843922982
##  [23,]       4        3  0.1333513903
##  [24,]       2        3  0.3631265862
##  [25,]       4        3  0.0234768317
##  [26,]       2        3  0.3051132267
##  [27,]       2        3  0.3631265862
##  [28,]       3        2  0.3370931029
##  [29,]       3        2  0.3370931029
##  [30,]       2        3  0.3756957496
##  [31,]       2        3  0.3756957496
##  [32,]       2        3  0.1056070099
##  [33,]       3        2  0.4205223312
##  [34,]       4        3 -0.0116896781
##  [35,]       3        2  0.3751550987
##  [36,]       3        2  0.4157159730
##  [37,]       3        2  0.3353487232
##  [38,]       2        3  0.3051132267
##  [39,]       3        2  0.3448566662
##  [40,]       2        3  0.2808920685
##  [41,]       2        3  0.3051132267
##  [42,]       3        4  0.3086148538
##  [43,]       3        2  0.4186900160
##  [44,]       3        2  0.4157159730
##  [45,]       3        4  0.2095364304
##  [46,]       2        3  0.1521961693
##  [47,]       3        4  0.2203015443
##  [48,]       3        2  0.3421148586
##  [49,]       3        4  0.3566249540
##  [50,]       3        2  0.3191965360
##  [51,]       3        2  0.4514986981
##  [52,]       2        3  0.0987463970
##  [53,]       3        4  0.2766583685
##  [54,]       2        3  0.0978254590
##  [55,]       3        4  0.0854508295
##  [56,]       1        3  0.2048047576
##  [57,]       3        2  0.3577338600
##  [58,]       3        2  0.3160150888
##  [59,]       4        3  0.1934219952
##  [60,]       2        3  0.3051132267
##  [61,]       4        2 -0.0292332083
##  [62,]       3        2  0.2316179183
##  [63,]       3        4  0.3286244221
##  [64,]       2        3  0.3837615843
##  [65,]       3        4  0.3410541492
##  [66,]       2        3  0.2000466951
##  [67,]       3        2  0.4360099411
##  [68,]       2        3  0.2412791030
##  [69,]       3        4  0.1937490576
##  [70,]       2        3  0.2414145353
##  [71,]       2        3  0.2670185294
##  [72,]       3        4  0.2690535594
##  [73,]       3        2  0.3448566662
##  [74,]       3        2  0.3246107255
##  [75,]       3        4  0.2766583685
##  [76,]       3        4  0.2400592376
##  [77,]       4        3  0.1236092962
##  [78,]       2        3  0.3756957496
##  [79,]       3        2  0.4143635812
##  [80,]       3        4  0.2866663812
##  [81,]       2        3  0.1593700443
##  [82,]       2        3  0.3051132267
##  [83,]       3        2  0.3246107255
##  [84,]       3        2  0.3556609037
##  [85,]       3        2  0.4360099411
##  [86,]       4        3 -0.0102966040
##  [87,]       3        2  0.4360099411
##  [88,]       3        2  0.4168140203
##  [89,]       3        4  0.3488714356
##  [90,]       3        2  0.4432199819
##  [91,]       3        2  0.4135559672
##  [92,]       3        2  0.3160150888
##  [93,]       2        3  0.3051132267
##  [94,]       3        2  0.3421148586
##  [95,]       3        2  0.3178697032
##  [96,]       2        3  0.3051132267
##  [97,]       2        3  0.3051132267
##  [98,]       3        4  0.2242897011
##  [99,]       3        2  0.3322443591
## [100,]       2        3  0.3051132267
## [101,]       4        3  0.1236092962
## [102,]       3        4  0.2719528730
## [103,]       3        2  0.3448566662
## [104,]       3        2  0.4096950173
## [105,]       2        3  0.3051132267
## [106,]       3        2  0.4157159730
## [107,]       3        2  0.4360099411
## [108,]       4        3 -0.0024988312
## [109,]       3        2  0.4289599714
## [110,]       3        2  0.4289599714
## [111,]       2        3  0.3631265862
## [112,]       4        3  0.1772789987
## [113,]       4        3  0.1883580855
## [114,]       3        2  0.2370504099
## [115,]       3        2  0.2173530281
## [116,]       3        2  0.4096950173
## [117,]       3        2  0.1538076812
## [118,]       3        2  0.1251236801
## [119,]       2        3  0.3051132267
## [120,]       4        3  0.1700992748
## [121,]       2        3  0.3631265862
## [122,]       4        3  0.1236092962
## [123,]       3        2  0.3160150888
## [124,]       3        4  0.2115676111
## [125,]       4        3  0.1470799086
## [126,]       3        2  0.4096950173
## [127,]       3        4  0.3764058542
## [128,]       4        1  0.1046329575
## [129,]       3        4  0.2114101858
## [130,]       3        4  0.2719528730
## [131,]       2        3  0.2082425793
## [132,]       3        2  0.3448566662
## [133,]       2        3  0.3959911058
## [134,]       4        3  0.2422380639
## [135,]       3        2  0.3341522301
## [136,]       3        2  0.3222937255
## [137,]       3        4  0.2719528730
## [138,]       3        2  0.3448566662
## [139,]       4        3  0.2416601282
## [140,]       3        2  0.1985535887
## [141,]       3        2  0.3246107255
## [142,]       3        2  0.3002735074
## [143,]       2        3  0.2414145353
## [144,]       4        3 -0.0816274265
## [145,]       2        3  0.2000466951
## [146,]       3        2  0.3072379977
## [147,]       2        3  0.2000466951
## [148,]       3        2  0.4360099411
## [149,]       3        4  0.2182118228
## [150,]       3        2  0.3448566662
## [151,]       3        4  0.2191412314
## [152,]       3        2  0.3370931029
## [153,]       4        3  0.0975361528
## [154,]       3        4  0.1711871100
## [155,]       2        3  0.2808920685
## [156,]       2        3  0.3537674381
## [157,]       2        3  0.2534437344
## [158,]       3        2  0.4432199819
## [159,]       3        4  0.3384994725
## [160,]       2        3  0.2534437344
## [161,]       3        2  0.4357034195
## [162,]       4        1  0.0782222102
## [163,]       4        3  0.0321936106
## [164,]       2        3  0.1123953743
## [165,]       3        2  0.1810663944
## [166,]       1        3  0.2011513347
## [167,]       3        2  0.2723337128
## [168,]       4        3  0.1973441961
## [169,]       3        4  0.2858234707
## [170,]       3        2  0.3556609037
## [171,]       4        3 -0.0097766315
## [172,]       3        2  0.3421148586
## [173,]       4        3  0.0485282651
## [174,]       2        3  0.3631265862
## [175,]       1        3  0.1644198654
## [176,]       3        4  0.2346603294
## [177,]       3        4  0.1421870979
## [178,]       2        3  0.3631265862
## [179,]       4        3  0.3063290523
## [180,]       3        2  0.3205937304
## [181,]       3        2  0.4135559672
## [182,]       3        1  0.1085618081
## [183,]       2        3  0.1908944637
## [184,]       2        3  0.3756957496
## [185,]       2        3  0.3756957496
## [186,]       3        1  0.3090953782
## [187,]       3        1  0.3709975307
## [188,]       1        3  0.1838418148
## [189,]       4        2  0.0033491946
## [190,]       2        3  0.1943099322
## [191,]       2        3  0.3959911058
## [192,]       3        1  0.1139115073
## [193,]       3        1  0.3285045151
## [194,]       3        4  0.3817568832
## [195,]       2        3  0.3631265862
## [196,]       3        2  0.4165710443
## [197,]       4        1  0.1140629111
## [198,]       3        2  0.3994599055
## [199,]       2        3  0.3631265862
## [200,]       3        2  0.2850923887
## [201,]       2        3  0.3234134334
## [202,]       2        3  0.2578677731
## [203,]       4        3  0.2840206904
## [204,]       4        3  0.0024791598
## [205,]       4        3  0.1116746067
## [206,]       4        3  0.2873945042
## [207,]       1        3  0.1339765665
## [208,]       3        1  0.1430400741
## [209,]       1        3  0.0316812915
## [210,]       2        3  0.3434397729
## [211,]       4        2  0.2966087761
## [212,]       4        3  0.2239276534
## [213,]       3        2  0.3994599055
## [214,]       2        3  0.1661319395
## [215,]       4        2  0.2452515956
## [216,]       2        3  0.3756957496
## [217,]       2        3  0.3299009774
## [218,]       3        2  0.3991759923
## [219,]       2        3  0.1526908910
## [220,]       3        4  0.3462505765
## [221,]       3        2  0.2703312073
## [222,]       2        3  0.1697761948
## [223,]       3        2  0.4432199819
## [224,]       4        3  0.1849398784
## [225,]       2        3  0.3631265862
## [226,]       2        3  0.2414145353
## [227,]       2        3  0.3756957496
## [228,]       3        1  0.3480985467
## [229,]       3        2  0.3550340297
## [230,]       3        4  0.2690535594
## [231,]       2        3  0.2508363909
## [232,]       3        4  0.1941119721
## [233,]       3        2  0.3421148586
## [234,]       4        3  0.0597976346
## [235,]       3        1  0.3691991572
## [236,]       2        3  0.2665442594
## [237,]       3        2  0.2666922915
## [238,]       2        1  0.0637076285
## [239,]       1        3  0.0005639306
## [240,]       3        2  0.2532062416
## [241,]       3        1  0.3248531452
## [242,]       3        1  0.1612055644
## [243,]       4        3  0.2694164727
## [244,]       3        1  0.2957061997
## [245,]       2        1  0.2816346177
## [246,]       3        1  0.4391376709
## [247,]       1        3 -0.0547117586
## [248,]       3        1  0.2497298189
## [249,]       1        3  0.2965974936
## [250,]       3        1  0.4173362572
## [251,]       2        3  0.4006433462
## [252,]       3        2  0.1567773785
## [253,]       3        2  0.2948731213
## [254,]       3        2  0.2665825477
## [255,]       1        3  0.1451224263
## [256,]       4        3  0.2737155571
## [257,]       3        4  0.1701576144
## [258,]       4        3  0.2848455033
## [259,]       3        1  0.2406550817
## [260,]       4        3  0.3213561301
## [261,]       3        2  0.2484893976
## [262,]       2        3  0.2396966396
## [263,]       1        2  0.0092179555
## [264,]       3        1  0.2629988888
## [265,]       3        4  0.1691808055
## [266,]       3        1  0.4352026034
## [267,]       3        1  0.1780370301
## [268,]       4        3  0.2646247665
## [269,]       3        2  0.3197301590
## [270,]       4        3  0.3029783486
## [271,]       3        2  0.3632930401
## [272,]       3        4  0.2541694768
## [273,]       4        3  0.0091229609
## [274,]       2        3  0.1514630508
## [275,]       3        2  0.2355908550
## [276,]       3        4  0.2287993101
## [277,]       3        4  0.3778212707
## [278,]       4        3  0.0036064311
## [279,]       4        3  0.1846081085
## [280,]       4        3  0.2164847065
## [281,]       3        2  0.2044678809
## [282,]       2        3  0.3929095679
## [283,]       1        4  0.2769256202
## [284,]       3        1  0.4152390557
## [285,]       3        2  0.3229155251
## [286,]       3        4  0.3408840819
## [287,]       3        1  0.2957061997
## [288,]       1        3 -0.0420817875
## [289,]       1        3  0.1175810007
## [290,]       3        1  0.4391376709
## [291,]       3        2  0.3327535043
## [292,]       2        1  0.1338418398
## [293,]       3        4  0.2541694768
## [294,]       2        3  0.4108293856
## [295,]       3        2  0.4303443670
## [296,]       3        1  0.2945473624
## [297,]       3        4  0.3778212707
## [298,]       3        1  0.4092573756
## [299,]       2        3  0.2396966396
## [300,]       2        3  0.4501229338
## [301,]       3        2  0.2874817691
## [302,]       4        2  0.1491052038
## [303,]       3        1  0.2945473624
## [304,]       3        2  0.3113359871
## [305,]       1        3 -0.0367467945
## [306,]       3        4  0.2594552454
## [307,]       3        2  0.2865595159
## [308,]       3        2  0.3004833565
## [309,]       1        3  0.1668465317
## [310,]       1        3  0.1358391134
## [311,]       3        2  0.3197301590
## [312,]       1        3  0.1361252717
## [313,]       2        3  0.3784515597
## [314,]       4        3  0.0533716117
## [315,]       3        1  0.4001093494
## [316,]       1        3 -0.0547117586
## [317,]       4        1  0.0195726295
## [318,]       3        1  0.4391376709
## [319,]       3        2  0.2665825477
## [320,]       3        2  0.4303443670
## [321,]       3        1  0.2618188985
## [322,]       4        3  0.1521455558
## [323,]       3        4  0.2033831847
## [324,]       2        3  0.2968106899
## [325,]       3        2  0.1903942437
## [326,]       3        4  0.3806680481
## [327,]       3        2  0.2540701949
## [328,]       2        1  0.2538490004
## [329,]       2        3  0.3968577339
## [330,]       3        2  0.3197414375
## [331,]       3        2  0.2716873143
## [332,]       1        3 -0.0534847228
## [333,]       2        3  0.1870792589
## [334,]       3        4  0.1769485428
## [335,]       3        1  0.2621129610
## [336,]       4        3  0.2122369790
## [337,]       3        1  0.4391376709
## [338,]       2        3  0.4108293856
## [339,]       2        3  0.2968106899
## [340,]       4        3  0.1268743000
## [341,]       2        1  0.2685756935
## [342,]       2        3  0.4108293856
## [343,]       3        4  0.3229678351
## [344,]       4        3  0.2289835299
## [345,]       4        3  0.1734217262
## [346,]       3        2  0.3251254866
## [347,]       3        1  0.4352026034
## [348,]       3        1  0.2957061997
## [349,]       3        1  0.4001093494
## [350,]       3        1  0.2945473624
## [351,]       3        1  0.4001093494
## [352,]       2        3  0.4360862262
## [353,]       4        3  0.0645826143
## [354,]       3        2  0.2321800353
## [355,]       3        1  0.2957061997
## [356,]       2        3  0.4501229338
## [357,]       3        1  0.3591246298
## [358,]       2        1  0.2538490004
## [359,]       3        1  0.3975940329
## [360,]       1        3  0.2161982868
## [361,]       3        1  0.4391376709
## [362,]       3        1  0.4352026034
## [363,]       2        3  0.4501229338
## [364,]       3        2  0.2484893976
## [365,]       3        4  0.3806680481
## [366,]       1        3  0.2012773367
## [367,]       3        1  0.3552747787
## [368,]       2        3  0.2239633438
## [369,]       3        1  0.2198445154
## [370,]       3        2  0.1992433193
## [371,]       4        3  0.0271293346
## [372,]       3        2  0.2080468794
## [373,]       3        2  0.3197301590
## [374,]       3        1  0.0717198235
## [375,]       3        2  0.2979620126
## [376,]       3        1  0.4001093494
## [377,]       3        1  0.2629988888
## [378,]       3        1  0.2626960454
## [379,]       2        3  0.3968577339
## [380,]       3        1  0.2223377766
## [381,]       3        4  0.3297049159
## [382,]       3        2  0.3197301590
## [383,]       3        1  0.4152390557
## [384,]       2        3  0.3968577339
## [385,]       3        4  0.1628490764
## [386,]       3        2  0.3251254866
## [387,]       1        3  0.2373506132
## [388,]       2        3  0.4108293856
## [389,]       2        3  0.2396966396
## [390,]       3        4  0.2690872875
## [391,]       3        2  0.2665825477
## [392,]       3        4  0.2033831847
## [393,]       3        2  0.3368742814
## [394,]       3        4  0.2728441797
## [395,]       3        2  0.2665825477
## [396,]       3        2  0.2948731213
## [397,]       3        2  0.1567773785
## [398,]       2        3  0.4006433462
## [399,]       3        1  0.4173362572
## [400,]       1        3  0.2965974936
## [401,]       3        1  0.2497298189
## [402,]       1        3 -0.0547117586
## [403,]       3        1  0.4391376709
## [404,]       2        1  0.2816346177
## [405,]       3        1  0.2957061997
## [406,]       4        3  0.2694164727
## [407,]       3        1  0.2945473624
## [408,]       2        3  0.2396966396
## [409,]       3        2  0.2539335960
## [410,]       3        1  0.1295661997
## [411,]       3        1  0.1612212312
## [412,]       1        3  0.0539293140
## [413,]       2        3  0.4360055022
## [414,]       2        3  0.4360055022
## [415,]       3        1  0.1114671034
## [416,]       4        1  0.1629672752
## [417,]       3        1  0.1016129096
## [418,]       2        3  0.2718666230
## [419,]       3        1  0.1314591229
## [420,]       1        3  0.1369838054
## [421,]       1        3  0.2576497126
## [422,]       3        1  0.0859963726
## [423,]       2        3  0.4104091322
## [424,]       1        3  0.1140560714
## [425,]       3        1  0.1123850179
## [426,]       3        1  0.1156324019
## [427,]       1        3  0.0886787013
## [428,]       1        3  0.2538185153
## [429,]       1        2  0.2838465530
## [430,]       2        3  0.3125884652
## [431,]       2        3  0.4360055022
## [432,]       2        3  0.4477035829
## [433,]       3        1  0.1612212312
## [434,]       2        3  0.4360055022
## [435,]       2        3  0.4477035829
## [436,]       2        1  0.3901102634
## [437,]       1        3  0.1286539422
## [438,]       3        1  0.1308511105
## [439,]       2        3  0.2771387412
## [440,]       2        3  0.4477035829
## [441,]       2        3  0.4360055022
## [442,]       3        1  0.1578843159
## [443,]       2        1  0.3901102634
## [444,]       3        1  0.1612212312
## [445,]       2        1  0.2156666715
## [446,]       2        3  0.4104091322
## [447,]       1        2  0.2624974047
## [448,]       3        1  0.1578843159
## [449,]       1        3  0.0786999740
## [450,]       3        1  0.1156324019
## [451,]       2        1  0.3901102634
## [452,]       3        1  0.0870758619
## [453,]       4        1  0.1894556519
## [454,]       1        3  0.2229653116
## [455,]       1        2  0.0686880486
## [456,]       2        1  0.1999635960
## [457,]       1        3  0.3727643530
## [458,]       3        1  0.3130303880
## [459,]       4        1  0.1868544065
## [460,]       1        3  0.0539293140
## [461,]       1        3  0.1095986014
## [462,]       2        3  0.4477035829
## [463,]       3        2  0.2454843255
## [464,]       4        3  0.1771782016
## [465,]       2        1  0.2321205143
## [466,]       1        3  0.0808227324
## [467,]       3        1  0.1486757421
## [468,]       4        3  0.2073236624
## [469,]       3        2  0.1122904577
## [470,]       2        1  0.3560680218
## [471,]       3        1  0.1353435697
## [472,]       1        3  0.1236282734
## [473,]       4        3  0.0685032867
## [474,]       1        3  0.1165052747
## [475,]       1        3  0.0489749586
## [476,]       4        1 -0.0087016881
## [477,]       1        2 -0.0621887925
## [478,]       3        1  0.1424024918
## [479,]       1        3  0.3028216019
## [480,]       4        3 -0.1150773741
## [481,]       3        1  0.1578843159
## [482,]       4        3  0.0280136704
## [483,]       1        3  0.0584621055
## [484,]       1        3  0.3415737726
## [485,]       1        3  0.3616960882
## [486,]       2        1  0.2107426380
## [487,]       2        1  0.3727459590
## [488,]       2        1  0.3560680218
## [489,]       1        3  0.0590823014
## [490,]       2        1  0.3394693738
## [491,]       1        3  0.1056300165
## [492,]       1        3  0.1285860553
## [493,]       1        3  0.0785552219
## [494,]       1        3  0.3102650528
## [495,]       2        1  0.1593429016
## [496,]       1        3  0.2266462734
## [497,]       1        3  0.3108047809
## [498,]       1        3  0.2387436044
## [499,]       2        1  0.3560680218
## [500,]       2        1  0.2032912753
## [501,]       1        3  0.2792942024
## [502,]       2        1  0.3560276472
## [503,]       1        3  0.2326183125
## [504,]       2        3  0.1558176889
## [505,]       2        1  0.3901102634
## [506,]       1        3  0.2346157631
## [507,]       1        2  0.2199310583
## [508,]       1        2  0.0943515563
## [509,]       1        3  0.1083406998
## [510,]       2        1  0.2243826060
## [511,]       1        3  0.1369838054
## [512,]       2        1  0.3727459590
## [513,]       2        1  0.3560680218
## [514,]       1        3  0.1080848406
## [515,]       1        3  0.1059689289
## [516,]       1        3  0.3108047809
## [517,]       1        3  0.0951075541
## [518,]       1        3  0.1286539422
## [519,]       2        3  0.2824881714
## [520,]       1        2  0.3095697502
## [521,]       2        1  0.3901102634
## [522,]       2        1  0.1968336648
## [523,]       1        3  0.2578776830
## [524,]       2        1  0.2470788241
## [525,]       1        3  0.3502487631
## [526,]       2        1  0.3394693738
## [527,]       1        2 -0.0197430845
## [528,]       1        3  0.3867588016
## [529,]       1        3  0.3867588016
## [530,]       1        3  0.1992377012
## [531,]       1        4  0.3574872571
## [532,]       1        3  0.2081520216
## [533,]       2        1  0.1649589268
## [534,]       1        3  0.2392197792
## [535,]       1        3  0.2613396322
## [536,]       1        3  0.2613396322
## [537,]       2        1  0.2487359433
## [538,]       1        3  0.2613396322
## [539,]       2        1  0.1048703202
## [540,]       2        1  0.2022919337
## [541,]       2        1  0.0929914108
## [542,]       1        3  0.2454735793
## [543,]       4        1  0.1622456911
## [544,]       1        3  0.3283464134
## [545,]       2        1  0.2930560685
## [546,]       2        1  0.1244355070
## [547,]       2        1  0.1059170261
## [548,]       1        4  0.0723861139
## [549,]       1        2  0.2435133686
## [550,]       1        2  0.0335590915
## [551,]       1        3  0.3590987649
## [552,]       1        3  0.2613396322
## [553,]       1        3  0.3364369775
## [554,]       1        2  0.3146715486
## [555,]       1        3  0.3867588016
## [556,]       1        3  0.2159269390
## [557,]       1        2  0.0249186053
## [558,]       1        3  0.1753704766
## [559,]       1        2  0.3427187074
## [560,]       1        3  0.2454735793
## [561,]       1        3  0.2039042377
## [562,]       1        3  0.1423706509
## [563,]       1        2  0.0183526229
## [564,]       1        3  0.1813505997
## [565,]       1        3  0.3637681383
## [566,]       1        3  0.1706247076
## [567,]       1        3  0.2293799627
## [568,]       1        3  0.3867588016
## [569,]       1        3  0.2526169549
## [570,]       1        2  0.1239184693
## [571,]       2        1  0.2686029812
## [572,]       1        2  0.3483750797
## [573,]       1        3  0.3213336235
## [574,]       1        3  0.2715606842
## [575,]       2        1  0.2930560685
## [576,]       1        3  0.2204796961
## [577,]       1        3  0.2445212751
## [578,]       1        3  0.2392197792
## [579,]       2        1  0.1141076172
## [580,]       1        3  0.2985951218
## [581,]       4        1  0.0913410792
## [582,]       1        2  0.0140136806
## [583,]       1        2  0.3464867242
## [584,]       2        1  0.2331754125
## [585,]       1        3  0.2613396322
## [586,]       2        1  0.1327486453
## [587,]       1        3  0.2613396322
## [588,]       1        3  0.2613396322
## [589,]       1        3  0.2332215497
## [590,]       1        4  0.0966740680
## [591,]       1        3  0.2293799627
## [592,]       2        1  0.1488791795
## [593,]       1        3  0.3742061955
## [594,]       1        4  0.0908492688
## [595,]       1        3  0.3283464134
## [596,]       1        3  0.3867588016
## [597,]       1        3  0.1782394482
## [598,]       1        3  0.3092992330
## [599,]       4        3  0.1791229344
## [600,]       2        1  0.1244355070
## [601,]       2        1  0.2686029812
## [602,]       1        3  0.2466267430
## [603,]       4        1  0.0937958536
## [604,]       1        3  0.0388620340
## [605,]       1        3  0.4114526170
## [606,]       2        1  0.2764985076
## [607,]       1        3  0.3291276736
## [608,]       2        1  0.2930560685
## [609,]       2        1  0.1048703202
## [610,]       1        3  0.1666096937
## [611,]       1        3  0.1342001946
## [612,]       1        3  0.2398395874
## [613,]       1        3  0.1772372521
## [614,]       1        3  0.1891190602
## [615,]       2        1  0.2686029812
## [616,]       2        1  0.1244355070
## [617,]       1        3  0.2236650373
## [618,]       1        2  0.0183526229
## [619,]       1        3  0.2022796765
## [620,]       1        3  0.2454735793
## [621,]       1        3  0.3637681383
## [622,]       2        1  0.1244355070
## [623,]       1        2  0.1466516325
## [624,]       1        3  0.2613396322
## [625,]       1        3  0.2138636940
## [626,]       1        3  0.4245404082
## [627,]       4        1  0.1041472018
## [628,]       1        3  0.1835278889
## [629,]       1        3  0.2466267430
## [630,]       1        2  0.3908120551
## [631,]       1        2  0.2461147491
## [632,]       1        2  0.0249186053
## [633,]       1        2  0.0249186053
## [634,]       1        3  0.2984279629
## [635,]       1        3  0.3742061955
## [636,]       1        2  0.0249186053
## [637,]       1        2  0.1488402902
## [638,]       1        3  0.1603913356
## [639,]       1        2  0.0251935595
## [640,]       2        1  0.2442618582
## [641,]       1        4  0.0645023262
## [642,]       2        1  0.1649589268
## [643,]       2        1  0.1488791795
## [644,]       1        2  0.2400174243
## [645,]       1        3  0.4192909658
## [646,]       2        1  0.1490812090
## [647,]       1        3  0.3867588016
## [648,]       4        1  0.1194899633
## [649,]       1        2  0.1183090730
## [650,]       1        2  0.1736602507
## [651,]       1        2 -0.0207636130
## [652,]       1        4  0.1003598682
## [653,]       1        3  0.4064630992
## [654,]       2        1  0.1488791795
## [655,]       1        3  0.3867588016
## [656,]       1        3  0.4210273585
## [657,]       1        3  0.3518436637
## [658,]       4        1  0.1677077216
## [659,]       1        3  0.1658048245
## [660,]       1        2  0.1161170739
## [661,]       1        3  0.3467686799
## [662,]       1        2  0.0812434352
## [663,]       1        3  0.4114526170
## [664,]       1        3  0.3201748289
## [665,]       1        3  0.2899393557
## [666,]       1        3  0.1666096937
## [667,]       1        3  0.3867588016
## [668,]       1        3  0.4192909658
## [669,]       1        2  0.0170973994
## [670,]       1        2 -0.0207636130
## [671,]       1        3  0.3569346947
## [672,]       1        2 -0.0246545901
## [673,]       1        3  0.2899393557
## [674,]       1        2  0.3523650934
## [675,]       1        2  0.0183526229
## [676,]       1        2  0.0071693994
## [677,]       4        1  0.1185685511
## attr(,"Ordered")
## [1] FALSE
## attr(,"call")
## silhouette.default(x = km$cluster, dist = dist(predictors))
## attr(,"class")
## [1] "silhouette"

#decrase the cluster by 1 cause on label encoding we start with 0
nc<-km$cluster-1

# bcubed precision and recall 
bcubed(nc, df$depressiveness)

## precision    recall 
## 0.7039766 0.3276772

performing operations to analyze the differences between the Actual and cluster variables.

df1<-data.frame(Actual=df$depressiveness,cluster=nc)
head(df1,10)

##    Actual cluster
## 1       1       2
## 2       1       2
## 3       1       2
## 4       0       3
## 5       1       2
## 6       1       1
## 7       1       2
## 8       1       1
## 9       0       2
## 10      1       2

#length 
length(df1$Actual)

## [1] 677

length(df1$cluster)

## [1] 677

#count number of cluster with matched vlaues 
cont_table <- table(df1$Actual, df1$cluster)
cont_table

##    
##       0   1   2   3
##   0  51   1  77  65
##   1 137 162 174  10

differences <- sum(apply(cont_table, 1, max)) - sum(diag(cont_table))
print(paste("difference: ",differences))

## [1] "difference:  38"

Cluster with k=3

#perform k-means clustering with k = 3 clusters
km <- kmeans(predictors, centers = 3, nstart = 25)

#view results
km

## K-means clustering with 3 clusters of sizes 310, 203, 164
## 
## Cluster means:
##   school_year      age    gender      bmi  who_bmi  phq_score
## 1    1.564516 19.10968 0.4290323 1.390323 3.125806 1.51290323
## 2    3.413793 22.12808 0.4285714 1.408867 3.246305 1.25615764
## 3    2.152439 19.92683 0.6219512 1.274390 3.176829 0.02439024
##   depression_severity  suicidal depression_diagnosis depression_treatment
## 1           0.5322581 0.8645161            0.9258065            0.9290323
## 2           0.7832512 0.9113300            0.8669951            0.9014778
## 3           3.0060976 0.9878049            0.9512195            0.9573171
##   gad_score anxiety_severity anxiousness anxiety_diagnosis anxiety_treatment
## 1 1.3419355        1.1548387   0.5935484         0.9258065         0.9096774
## 2 1.1280788        0.7783251   0.7536946         0.8965517         0.9162562
## 3 0.2682927        1.5000000   0.9878049         0.9512195         0.9512195
##   epworth_score sleepiness
## 1    0.23225806  0.7612903
## 2    0.21674877  0.7733990
## 3    0.04878049  0.9268293
## 
## Clustering vector:
##   [1] 1 1 1 1 1 3 1 3 1 1 3 1 1 1 1 1 1 1 3 1 3 1 1 3 1 3 3 1 1 3 3 3 1 1 1 1 1
##  [38] 3 1 3 3 1 1 1 1 3 1 1 1 1 1 3 1 3 1 2 1 1 1 3 1 1 1 3 1 3 1 3 1 3 3 1 1 1
##  [75] 1 1 1 3 1 1 3 3 1 1 1 1 1 1 1 1 1 1 3 1 1 3 3 1 1 3 1 1 1 1 3 1 1 1 1 1 3
## [112] 1 1 1 1 1 1 1 3 1 3 1 1 1 1 1 1 2 1 1 3 1 3 1 1 1 1 1 1 1 1 1 3 1 3 1 3 1
## [149] 1 1 1 1 1 1 3 3 3 1 1 3 1 2 1 3 1 2 1 1 1 1 1 1 1 3 2 1 1 3 1 1 1 1 3 3 3
## [186] 1 1 2 3 3 3 1 1 1 3 1 2 1 3 1 3 3 1 1 1 1 2 1 2 3 1 1 1 3 1 3 3 1 3 1 1 3
## [223] 1 1 3 3 3 1 1 1 3 1 1 1 1 3 1 3 2 1 1 1 1 1 3 1 2 1 2 1 3 1 1 1 2 1 1 1 1
## [260] 1 1 3 2 1 1 1 1 1 1 1 1 1 1 3 1 1 1 1 1 1 1 3 2 1 1 1 1 2 2 1 1 3 1 3 1 1
## [297] 1 1 3 3 1 1 1 1 2 1 1 1 2 2 1 2 3 1 1 2 2 1 1 1 1 1 1 3 1 1 1 3 3 1 1 2 3
## [334] 1 1 1 1 3 3 1 3 3 1 1 1 1 1 1 1 1 1 3 1 1 1 3 1 3 1 2 1 1 3 1 1 2 1 3 1 1
## [371] 1 1 1 1 1 1 1 1 3 1 1 1 1 3 1 1 2 3 3 1 1 1 1 1 1 1 1 3 1 2 1 2 1 3 1 1 1
## [408] 3 1 1 1 2 3 3 1 2 1 3 1 2 2 1 3 2 1 1 2 2 2 3 3 3 1 3 3 3 2 1 3 3 3 1 3 1
## [445] 3 3 2 1 2 1 3 1 2 2 2 3 2 1 2 2 2 3 1 1 3 2 1 1 1 3 1 2 1 2 2 2 2 1 2 1 1
## [482] 1 2 2 2 3 3 3 2 3 2 2 2 2 3 2 2 2 3 3 2 3 2 3 3 2 2 2 2 3 2 3 3 2 2 2 2 2
## [519] 3 2 3 3 2 3 2 3 2 2 2 2 2 2 3 2 2 2 3 2 3 3 3 2 2 2 3 3 3 2 2 2 2 2 2 2 2
## [556] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 3 2 2 2 3 2 2 2 3 2 2 2 2 3 2 3 2 2 2 2 2 3
## [593] 2 2 2 2 2 2 1 3 3 2 2 2 2 3 2 3 3 2 2 2 2 2 3 3 2 2 2 2 2 3 2 2 2 2 2 2 2
## [630] 2 2 2 2 2 2 2 2 2 2 3 2 3 3 2 2 3 2 2 2 2 2 2 2 3 2 2 2 2 2 2 2 2 2 2 2 2
## [667] 2 2 2 2 2 2 2 2 2 2 2
## 
## Within cluster sum of squares by cluster:
## [1] 2170.4387 1747.1823  907.7012
##  (between_SS / total_SS =  36.1 %)
## 
## Available components:
## 
## [1] "cluster"      "centers"      "totss"        "withinss"     "tot.withinss"
## [6] "betweenss"    "size"         "iter"         "ifault"

#plot results of k-means 
fviz_cluster(km, data = predictors)

clusplot(df, km$cluster, color = TRUE, shade = TRUE, labels = 3)

# Evaluate and compare the results
silhouette_score <- silhouette(km$cluster, dist(predictors))
silhouette_score

##        cluster neighbor    sil_width
##   [1,]       1        3  0.221390124
##   [2,]       1        3  0.328803751
##   [3,]       1        3  0.324023749
##   [4,]       1        3  0.215057944
##   [5,]       1        3  0.352433358
##   [6,]       3        1  0.330303818
##   [7,]       1        3  0.260334701
##   [8,]       3        1  0.252694193
##   [9,]       1        3  0.244781549
##  [10,]       1        3  0.250989127
##  [11,]       3        1  0.330303818
##  [12,]       1        3  0.239930958
##  [13,]       1        3  0.328803751
##  [14,]       1        3  0.335179375
##  [15,]       1        3  0.249969099
##  [16,]       1        3  0.377477828
##  [17,]       1        3  0.352727899
##  [18,]       1        3  0.248050648
##  [19,]       3        1  0.330303818
##  [20,]       1        3  0.347532090
##  [21,]       3        1  0.330303818
##  [22,]       1        3  0.372988784
##  [23,]       1        3  0.245138527
##  [24,]       3        1  0.385208741
##  [25,]       1        3  0.171779918
##  [26,]       3        1  0.330303818
##  [27,]       3        1  0.385208741
##  [28,]       1        3  0.260334701
##  [29,]       1        3  0.260334701
##  [30,]       3        1  0.398164046
##  [31,]       3        1  0.398164046
##  [32,]       3        1  0.148987595
##  [33,]       1        3  0.331697038
##  [34,]       1        3  0.286973542
##  [35,]       1        3  0.303962673
##  [36,]       1        3  0.336968739
##  [37,]       1        3  0.268162248
##  [38,]       3        1  0.330303818
##  [39,]       1        3  0.269775176
##  [40,]       3        1  0.306208303
##  [41,]       3        1  0.330303818
##  [42,]       1        3  0.347410376
##  [43,]       1        3  0.335435312
##  [44,]       1        3  0.336968739
##  [45,]       1        3  0.309984715
##  [46,]       3        1  0.189643598
##  [47,]       1        3  0.321987090
##  [48,]       1        3  0.260979093
##  [49,]       1        3  0.362100116
##  [50,]       1        3  0.243814303
##  [51,]       1        3  0.353871928
##  [52,]       3        1  0.140830674
##  [53,]       1        3  0.335179375
##  [54,]       3        1  0.136420176
##  [55,]       1        3  0.162326122
##  [56,]       2        3  0.204742918
##  [57,]       1        3  0.287751409
##  [58,]       1        3  0.239930958
##  [59,]       1        3  0.239126990
##  [60,]       3        1  0.330303818
##  [61,]       1        3 -0.009967044
##  [62,]       1        3  0.194650345
##  [63,]       1        3  0.365991844
##  [64,]       3        1  0.403185767
##  [65,]       1        2  0.317563183
##  [66,]       3        1  0.244056270
##  [67,]       1        3  0.344206372
##  [68,]       3        1  0.266278964
##  [69,]       1        2  0.195811389
##  [70,]       3        1  0.279810452
##  [71,]       3        1  0.292762937
##  [72,]       1        3  0.335103286
##  [73,]       1        3  0.269775176
##  [74,]       1        3  0.256295717
##  [75,]       1        3  0.335179375
##  [76,]       1        3  0.321416027
##  [77,]       1        3  0.268073925
##  [78,]       3        1  0.398164046
##  [79,]       1        3  0.331806041
##  [80,]       1        2  0.300864849
##  [81,]       3        1  0.178449134
##  [82,]       3        1  0.330303818
##  [83,]       1        3  0.256295717
##  [84,]       1        3  0.290907023
##  [85,]       1        3  0.344206372
##  [86,]       1        3  0.217089274
##  [87,]       1        3  0.344206372
##  [88,]       1        3  0.332084984
##  [89,]       1        3  0.352433358
##  [90,]       1        3  0.352727899
##  [91,]       1        3  0.327813225
##  [92,]       1        3  0.239930958
##  [93,]       3        1  0.330303818
##  [94,]       1        3  0.260979093
##  [95,]       1        3  0.256654141
##  [96,]       3        1  0.330303818
##  [97,]       3        1  0.330303818
##  [98,]       1        3  0.233891619
##  [99,]       1        3  0.271342421
## [100,]       3        1  0.330303818
## [101,]       1        3  0.268073925
## [102,]       1        3  0.343703460
## [103,]       1        3  0.269775176
## [104,]       1        3  0.325412290
## [105,]       3        1  0.330303818
## [106,]       1        3  0.336968739
## [107,]       1        3  0.344206372
## [108,]       1        3  0.292957123
## [109,]       1        3  0.344009765
## [110,]       1        3  0.344009765
## [111,]       3        1  0.385208741
## [112,]       1        3  0.260331509
## [113,]       1        3  0.189267960
## [114,]       1        3  0.190009838
## [115,]       1        3  0.176521176
## [116,]       1        3  0.325412290
## [117,]       1        3  0.109916371
## [118,]       1        3  0.079666840
## [119,]       3        1  0.330303818
## [120,]       1        3  0.261937894
## [121,]       3        1  0.385208741
## [122,]       1        3  0.268073925
## [123,]       1        3  0.239930958
## [124,]       1        3  0.325169631
## [125,]       1        3  0.251378640
## [126,]       1        3  0.325412290
## [127,]       1        3  0.377477828
## [128,]       2        1  0.076917950
## [129,]       1        3  0.323103164
## [130,]       1        3  0.343703460
## [131,]       3        1  0.252694193
## [132,]       1        3  0.269775176
## [133,]       3        1  0.415828930
## [134,]       1        3  0.073332223
## [135,]       1        3  0.250989127
## [136,]       1        3  0.260568597
## [137,]       1        3  0.343703460
## [138,]       1        3  0.269775176
## [139,]       1        3  0.149308578
## [140,]       1        3  0.156605592
## [141,]       1        3  0.256295717
## [142,]       1        3  0.240564797
## [143,]       3        1  0.279810452
## [144,]       1        3  0.234217452
## [145,]       3        1  0.244056270
## [146,]       1        3  0.256024172
## [147,]       3        1  0.244056270
## [148,]       1        3  0.344206372
## [149,]       1        3  0.226977055
## [150,]       1        3  0.269775176
## [151,]       1        3  0.291329503
## [152,]       1        3  0.260334701
## [153,]       1        3  0.267692847
## [154,]       1        3  0.266114836
## [155,]       3        1  0.306208303
## [156,]       3        1  0.373776555
## [157,]       3        1  0.279002613
## [158,]       1        3  0.352727899
## [159,]       1        3  0.337690974
## [160,]       3        1  0.279002613
## [161,]       1        3  0.344358192
## [162,]       2        1  0.079527162
## [163,]       1        2  0.119047556
## [164,]       3        1  0.141640132
## [165,]       1        3  0.145582913
## [166,]       2        3  0.203645125
## [167,]       1        3  0.202111069
## [168,]       1        3  0.260074314
## [169,]       1        2  0.282341739
## [170,]       1        3  0.290907023
## [171,]       1        3  0.207238776
## [172,]       1        3  0.260979093
## [173,]       1        3  0.281061503
## [174,]       3        1  0.385208741
## [175,]       2        1  0.180014230
## [176,]       1        3  0.316734358
## [177,]       1        3  0.201731931
## [178,]       3        1  0.385208741
## [179,]       1        3  0.176966356
## [180,]       1        3  0.263090920
## [181,]       1        3  0.327813225
## [182,]       1        2  0.088410792
## [183,]       3        1  0.208644844
## [184,]       3        1  0.398164046
## [185,]       3        1  0.398164046
## [186,]       1        2  0.246516820
## [187,]       1        2  0.294470346
## [188,]       2        3  0.184344266
## [189,]       3        1  0.051778390
## [190,]       3        1  0.230073961
## [191,]       3        1  0.415828930
## [192,]       1        2  0.098691117
## [193,]       1        2  0.262158640
## [194,]       1        3  0.381977366
## [195,]       3        1  0.385208741
## [196,]       1        3  0.332002637
## [197,]       2        3  0.053525281
## [198,]       1        3  0.315586569
## [199,]       3        1  0.385208741
## [200,]       1        3  0.239883443
## [201,]       3        1  0.346279034
## [202,]       3        1  0.290409191
## [203,]       1        3  0.060867972
## [204,]       1        3  0.294293189
## [205,]       1        3  0.148184933
## [206,]       1        3  0.207191304
## [207,]       2        1  0.135532750
## [208,]       1        2  0.103270227
## [209,]       2        1  0.054455518
## [210,]       3        1  0.362846732
## [211,]       1        3  0.046348538
## [212,]       1        3  0.231756446
## [213,]       1        3  0.315586569
## [214,]       3        1  0.185796260
## [215,]       1        3  0.048801037
## [216,]       3        1  0.398164046
## [217,]       3        1  0.345566917
## [218,]       1        3  0.313540389
## [219,]       3        1  0.164545293
## [220,]       1        3  0.361669293
## [221,]       1        3  0.198409111
## [222,]       3        1  0.203423793
## [223,]       1        3  0.352727899
## [224,]       1        2  0.140647092
## [225,]       3        1  0.385208741
## [226,]       3        1  0.279810452
## [227,]       3        1  0.398164046
## [228,]       1        2  0.276216149
## [229,]       1        3  0.283239149
## [230,]       1        3  0.335103286
## [231,]       3        1  0.271896747
## [232,]       1        3  0.296201267
## [233,]       1        3  0.260979093
## [234,]       1        2  0.114112278
## [235,]       1        2  0.292030073
## [236,]       3        1  0.289572795
## [237,]       1        3  0.194042946
## [238,]       3        2  0.074995525
## [239,]       2        1  0.031405454
## [240,]       1        3  0.199349294
## [241,]       1        2  0.258811348
## [242,]       1        2  0.104364960
## [243,]       1        3  0.183702481
## [244,]       1        2  0.213658454
## [245,]       3        2  0.298115411
## [246,]       1        3  0.343433453
## [247,]       2        1  0.008759033
## [248,]       1        2  0.181303993
## [249,]       2        1  0.294337781
## [250,]       1        3  0.331965148
## [251,]       3        1  0.417966010
## [252,]       1        3  0.102707109
## [253,]       1        3  0.222773867
## [254,]       1        3  0.183036710
## [255,]       2        1  0.150640083
## [256,]       1        3  0.138913284
## [257,]       1        2  0.223918026
## [258,]       1        3  0.182520203
## [259,]       1        2  0.174115364
## [260,]       1        3  0.163238888
## [261,]       1        3  0.172524954
## [262,]       3        1  0.281285120
## [263,]       2        3 -0.012223786
## [264,]       1        2  0.229552388
## [265,]       1        3  0.307027687
## [266,]       1        3  0.332552130
## [267,]       1        2  0.139558370
## [268,]       1        3  0.184945090
## [269,]       1        3  0.233273717
## [270,]       1        3  0.194084016
## [271,]       1        3  0.283593167
## [272,]       1        3  0.329468660
## [273,]       1        3  0.224980804
## [274,]       3        1  0.194936685
## [275,]       1        3  0.196625514
## [276,]       1        3  0.322708054
## [277,]       1        3  0.370556356
## [278,]       1        3  0.274918809
## [279,]       1        3  0.257225399
## [280,]       1        2  0.210768313
## [281,]       1        3  0.174796194
## [282,]       3        1  0.410429685
## [283,]       2        1  0.284907547
## [284,]       1        3  0.322698769
## [285,]       1        3  0.260293476
## [286,]       1        2  0.348664095
## [287,]       1        2  0.213658454
## [288,]       2        1  0.003021519
## [289,]       2        3  0.115478127
## [290,]       1        3  0.343433453
## [291,]       1        3  0.264373697
## [292,]       3        2  0.146418868
## [293,]       1        3  0.329468660
## [294,]       3        1  0.431666240
## [295,]       1        3  0.338154217
## [296,]       1        2  0.211898861
## [297,]       1        3  0.370556356
## [298,]       1        3  0.320332153
## [299,]       3        1  0.281285120
## [300,]       3        1  0.467627123
## [301,]       1        3  0.214927573
## [302,]       1        3  0.013284752
## [303,]       1        2  0.211898861
## [304,]       1        3  0.228948466
## [305,]       2        1 -0.012663295
## [306,]       1        3  0.318847574
## [307,]       1        3  0.217333764
## [308,]       1        3  0.230769862
## [309,]       2        1  0.180959974
## [310,]       2        1  0.146109264
## [311,]       1        3  0.233273717
## [312,]       2        1  0.138675131
## [313,]       3        1  0.395419289
## [314,]       1        3  0.278544821
## [315,]       1        3  0.311009224
## [316,]       2        1  0.008759033
## [317,]       2        1  0.054539260
## [318,]       1        3  0.343433453
## [319,]       1        3  0.183036710
## [320,]       1        3  0.338154217
## [321,]       1        2  0.202443986
## [322,]       1        3  0.131817296
## [323,]       1        2  0.246001637
## [324,]       3        1  0.332970379
## [325,]       1        3  0.150152852
## [326,]       1        3  0.361118961
## [327,]       1        3  0.188403594
## [328,]       3        2  0.270635485
## [329,]       3        1  0.417408021
## [330,]       1        3  0.241562434
## [331,]       1        3  0.191088794
## [332,]       2        1  0.008925852
## [333,]       3        1  0.206075023
## [334,]       1        3  0.298163729
## [335,]       1        2  0.188854549
## [336,]       1        3  0.255516566
## [337,]       1        3  0.343433453
## [338,]       3        1  0.431666240
## [339,]       3        1  0.332970379
## [340,]       1        3  0.262818474
## [341,]       3        2  0.279133870
## [342,]       3        1  0.431666240
## [343,]       1        2  0.351036291
## [344,]       1        2  0.187791193
## [345,]       1        3  0.125615890
## [346,]       1        3  0.241399505
## [347,]       1        3  0.332552130
## [348,]       1        2  0.213658454
## [349,]       1        3  0.311009224
## [350,]       1        2  0.211898861
## [351,]       1        3  0.311009224
## [352,]       3        1  0.453317056
## [353,]       1        2  0.200995177
## [354,]       1        3  0.179371627
## [355,]       1        2  0.213658454
## [356,]       3        1  0.467627123
## [357,]       1        3  0.279262136
## [358,]       3        2  0.270635485
## [359,]       1        3  0.322488782
## [360,]       2        1  0.224418287
## [361,]       1        3  0.343433453
## [362,]       1        3  0.332552130
## [363,]       3        1  0.467627123
## [364,]       1        3  0.172524954
## [365,]       1        3  0.361118961
## [366,]       2        1  0.207500143
## [367,]       1        3  0.277259808
## [368,]       3        2  0.236756373
## [369,]       1        2  0.164010627
## [370,]       1        3  0.148759144
## [371,]       1        3  0.169622483
## [372,]       1        3  0.164778778
## [373,]       1        3  0.233273717
## [374,]       1        2  0.043264852
## [375,]       1        3  0.219510150
## [376,]       1        3  0.311009224
## [377,]       1        2  0.229552388
## [378,]       1        2  0.230886800
## [379,]       3        1  0.417408021
## [380,]       1        2  0.193793999
## [381,]       1        3  0.333522028
## [382,]       1        3  0.233273717
## [383,]       1        3  0.322698769
## [384,]       3        1  0.417408021
## [385,]       1        2  0.232676372
## [386,]       1        3  0.241399505
## [387,]       2        1  0.239173265
## [388,]       3        1  0.431666240
## [389,]       3        1  0.281285120
## [390,]       1        2  0.293977126
## [391,]       1        3  0.183036710
## [392,]       1        2  0.246001637
## [393,]       1        3  0.265310140
## [394,]       1        2  0.313210348
## [395,]       1        3  0.183036710
## [396,]       1        3  0.222773867
## [397,]       1        3  0.102707109
## [398,]       3        1  0.417966010
## [399,]       1        3  0.331965148
## [400,]       2        1  0.294337781
## [401,]       1        2  0.181303993
## [402,]       2        1  0.008759033
## [403,]       1        3  0.343433453
## [404,]       3        2  0.298115411
## [405,]       1        2  0.213658454
## [406,]       1        3  0.183702481
## [407,]       1        2  0.211898861
## [408,]       3        1  0.281285120
## [409,]       1        3  0.180681383
## [410,]       1        2  0.117269371
## [411,]       1        2  0.107656547
## [412,]       2        1  0.087889200
## [413,]       3        1  0.451498912
## [414,]       3        1  0.451498912
## [415,]       1        2  0.062600659
## [416,]       2        1  0.105662517
## [417,]       1        2  0.058466889
## [418,]       3        2  0.294903939
## [419,]       1        2  0.062266719
## [420,]       2        1  0.168483411
## [421,]       2        1  0.258211498
## [422,]       1        2  0.078501551
## [423,]       3        1  0.426493242
## [424,]       2        1  0.135151471
## [425,]       1        2  0.051122596
## [426,]       1        2  0.065266039
## [427,]       2        1  0.116642140
## [428,]       2        1  0.250268770
## [429,]       2        3  0.269744256
## [430,]       3        1  0.328487681
## [431,]       3        1  0.451498912
## [432,]       3        1  0.463555239
## [433,]       1        2  0.107656547
## [434,]       3        1  0.451498912
## [435,]       3        1  0.463555239
## [436,]       3        2  0.398917895
## [437,]       2        1  0.162242541
## [438,]       1        2  0.101869416
## [439,]       3        1  0.290302517
## [440,]       3        1  0.463555239
## [441,]       3        1  0.451498912
## [442,]       1        2  0.105875468
## [443,]       3        2  0.398917895
## [444,]       1        2  0.107656547
## [445,]       3        2  0.237180728
## [446,]       3        1  0.426493242
## [447,]       2        3  0.254364591
## [448,]       1        2  0.105875468
## [449,]       2        1  0.080986129
## [450,]       1        2  0.065266039
## [451,]       3        2  0.398917895
## [452,]       1        2  0.052062619
## [453,]       2        1  0.002530591
## [454,]       2        1  0.232862653
## [455,]       2        3  0.039498483
## [456,]       3        2  0.221895160
## [457,]       2        1  0.369242100
## [458,]       1        2  0.239075904
## [459,]       2        3  0.031034594
## [460,]       2        1  0.087889200
## [461,]       2        1  0.139789125
## [462,]       3        1  0.463555239
## [463,]       1        3  0.176425545
## [464,]       1        2  0.118524263
## [465,]       3        2  0.245615023
## [466,]       2        1  0.095743978
## [467,]       1        2  0.116526951
## [468,]       1        2  0.133653857
## [469,]       1        3  0.065352657
## [470,]       3        2  0.365465166
## [471,]       1        2  0.064995845
## [472,]       2        1  0.155171605
## [473,]       1        2  0.120811351
## [474,]       2        1  0.149925799
## [475,]       2        1  0.075831060
## [476,]       2        1  0.055938968
## [477,]       2        3 -0.089848717
## [478,]       1        2  0.112965035
## [479,]       2        1  0.308569572
## [480,]       1        2  0.095664774
## [481,]       1        2  0.105875468
## [482,]       1        2  0.080503218
## [483,]       2        1  0.090752206
## [484,]       2        1  0.339445725
## [485,]       2        1  0.358715492
## [486,]       3        2  0.223540466
## [487,]       3        2  0.381751557
## [488,]       3        2  0.365465166
## [489,]       2        1  0.090066921
## [490,]       3        2  0.348982756
## [491,]       2        1  0.138536963
## [492,]       2        1  0.157970105
## [493,]       2        1  0.096015596
## [494,]       2        1  0.314552702
## [495,]       3        2  0.166492670
## [496,]       2        1  0.235542690
## [497,]       2        3  0.294265240
## [498,]       2        1  0.234873174
## [499,]       3        2  0.365465166
## [500,]       3        2  0.216091466
## [501,]       2        1  0.286500770
## [502,]       3        2  0.365442125
## [503,]       2        1  0.234712023
## [504,]       3        1  0.194288652
## [505,]       3        2  0.398917895
## [506,]       2        1  0.231666633
## [507,]       2        3  0.198758117
## [508,]       2        3  0.080499482
## [509,]       2        1  0.135611654
## [510,]       3        2  0.237858802
## [511,]       2        1  0.168483411
## [512,]       3        2  0.381751557
## [513,]       3        2  0.365465166
## [514,]       2        1  0.138531641
## [515,]       2        1  0.135741487
## [516,]       2        3  0.294265240
## [517,]       2        1  0.115117942
## [518,]       2        1  0.162242541
## [519,]       3        2  0.306318821
## [520,]       2        3  0.294408682
## [521,]       3        2  0.398917895
## [522,]       3        2  0.209865818
## [523,]       2        1  0.264802610
## [524,]       3        2  0.260442570
## [525,]       2        1  0.347938904
## [526,]       3        2  0.348982756
## [527,]       2        3 -0.037492700
## [528,]       2        1  0.380743382
## [529,]       2        1  0.380743382
## [530,]       2        1  0.202893677
## [531,]       2        3  0.368179816
## [532,]       2        1  0.221808191
## [533,]       3        2  0.181348752
## [534,]       2        1  0.250765333
## [535,]       2        1  0.270649731
## [536,]       2        1  0.270649731
## [537,]       3        2  0.260533262
## [538,]       2        1  0.270649731
## [539,]       3        2  0.130398058
## [540,]       3        2  0.213673444
## [541,]       3        2  0.115322126
## [542,]       2        1  0.251681076
## [543,]       2        1  0.055205649
## [544,]       2        1  0.323146249
## [545,]       3        2  0.304862800
## [546,]       3        2  0.150260860
## [547,]       3        2  0.132344171
## [548,]       2        1  0.129526961
## [549,]       2        3  0.228654176
## [550,]       2        3  0.006921305
## [551,]       2        1  0.354966216
## [552,]       2        1  0.270649731
## [553,]       2        1  0.336635712
## [554,]       2        3  0.298241917
## [555,]       2        1  0.380743382
## [556,]       2        1  0.223757939
## [557,]       2        3 -0.008227957
## [558,]       2        1  0.174916203
## [559,]       2        3  0.320962396
## [560,]       2        1  0.251681076
## [561,]       2        1  0.212508734
## [562,]       2        1  0.157565174
## [563,]       2        3 -0.013412037
## [564,]       2        1  0.194977769
## [565,]       2        1  0.359779344
## [566,]       2        1  0.185895749
## [567,]       2        1  0.236448029
## [568,]       2        1  0.380743382
## [569,]       2        1  0.252781222
## [570,]       2        3  0.109126991
## [571,]       3        2  0.280660105
## [572,]       2        3  0.326890791
## [573,]       2        1  0.317196020
## [574,]       2        1  0.267527999
## [575,]       3        2  0.304862800
## [576,]       2        1  0.233359705
## [577,]       2        1  0.255506885
## [578,]       2        1  0.250765333
## [579,]       3        2  0.129341316
## [580,]       2        1  0.294336661
## [581,]       2        1  0.158851003
## [582,]       2        3 -0.018246328
## [583,]       2        3  0.329998821
## [584,]       3        2  0.244946475
## [585,]       2        1  0.270649731
## [586,]       3        2  0.149076546
## [587,]       2        1  0.270649731
## [588,]       2        1  0.270649731
## [589,]       2        1  0.245243054
## [590,]       2        1  0.141725719
## [591,]       2        1  0.236448029
## [592,]       3        2  0.165106996
## [593,]       2        1  0.370107096
## [594,]       2        1  0.139145050
## [595,]       2        1  0.323146249
## [596,]       2        1  0.380743382
## [597,]       2        1  0.182787863
## [598,]       2        1  0.303686830
## [599,]       1        2  0.039549659
## [600,]       3        2  0.150260860
## [601,]       3        2  0.280660105
## [602,]       2        1  0.258583792
## [603,]       2        1  0.085234904
## [604,]       2        1  0.079836692
## [605,]       2        1  0.403017917
## [606,]       3        2  0.288505404
## [607,]       2        1  0.328049833
## [608,]       3        2  0.304862800
## [609,]       3        2  0.130398058
## [610,]       2        1  0.181393372
## [611,]       2        1  0.149901733
## [612,]       2        1  0.250411010
## [613,]       2        1  0.185836352
## [614,]       2        1  0.187136111
## [615,]       3        2  0.280660105
## [616,]       3        2  0.150260860
## [617,]       2        1  0.236775699
## [618,]       2        3 -0.013412037
## [619,]       2        1  0.210106247
## [620,]       2        1  0.251681076
## [621,]       2        1  0.359779344
## [622,]       3        2  0.150260860
## [623,]       2        3  0.128353263
## [624,]       2        1  0.270649731
## [625,]       2        1  0.221295653
## [626,]       2        1  0.415181066
## [627,]       2        1  0.138682631
## [628,]       2        1  0.187154575
## [629,]       2        1  0.258583792
## [630,]       2        3  0.368477585
## [631,]       2        3  0.227454344
## [632,]       2        3 -0.008227957
## [633,]       2        3 -0.008227957
## [634,]       2        3  0.287654394
## [635,]       2        1  0.370107096
## [636,]       2        3 -0.008227957
## [637,]       2        3  0.136803019
## [638,]       2        3  0.166782460
## [639,]       2        3  0.015060453
## [640,]       3        2  0.256092562
## [641,]       2        1  0.123435063
## [642,]       3        2  0.181348752
## [643,]       3        2  0.165106996
## [644,]       2        3  0.230941146
## [645,]       2        1  0.410714650
## [646,]       3        2  0.165670265
## [647,]       2        1  0.380743382
## [648,]       2        1  0.118199878
## [649,]       2        3  0.090177480
## [650,]       2        3  0.149227221
## [651,]       2        3 -0.040428577
## [652,]       2        1  0.182843918
## [653,]       2        1  0.398778132
## [654,]       3        2  0.165106996
## [655,]       2        1  0.380743382
## [656,]       2        3  0.404858411
## [657,]       2        1  0.347277807
## [658,]       2        3  0.068258526
## [659,]       2        1  0.168399559
## [660,]       2        3  0.086651267
## [661,]       2        1  0.342581571
## [662,]       2        3  0.064338641
## [663,]       2        1  0.403017917
## [664,]       2        1  0.318356735
## [665,]       2        3  0.274759123
## [666,]       2        1  0.181393372
## [667,]       2        1  0.380743382
## [668,]       2        1  0.410714650
## [669,]       2        3 -0.008528497
## [670,]       2        3 -0.040428577
## [671,]       2        1  0.354106355
## [672,]       2        3 -0.043685256
## [673,]       2        3  0.274759123
## [674,]       2        3  0.330088380
## [675,]       2        3 -0.013412037
## [676,]       2        3 -0.025731775
## [677,]       2        1  0.143071098
## attr(,"Ordered")
## [1] FALSE
## attr(,"call")
## silhouette.default(x = km$cluster, dist = dist(predictors))
## attr(,"class")
## [1] "silhouette"

#decrase the cluster by 1 cause on label encoding we start with 0
nc<-km$cluster-1

# bcubed precision and recall 
bcubed(nc, df$depressiveness)

## precision    recall 
## 0.6421454 0.3959195

performing operations to analyze the differences between the Actual and cluster variables.

df1<-data.frame(Actual=df$depressiveness,cluster=nc)
head(df1,10)

##    Actual cluster
## 1       1       0
## 2       1       0
## 3       1       0
## 4       0       0
## 5       1       0
## 6       1       2
## 7       1       0
## 8       1       2
## 9       0       0
## 10      1       0

#length 
length(df1$Actual)

## [1] 677

length(df1$cluster)

## [1] 677

#count number of cluster with matched vlaues 
cont_table <- table(df1$Actual, df1$cluster)
cont_table

##    
##       0   1   2
##   0 127  65   2
##   1 183 138 162

differences <- sum(apply(cont_table, 1, max)) - sum(diag(cont_table))
print(paste("difference: ",differences))

## [1] "difference:  45"